Background: Due to successful treatment modalities, the majority of pediatric cancer patients will survive. Increased body mass index (BMI) is a complication among pediatric cancer survivors. Methods: This retrospective single-center study examined BMI changes among a cohort of predominantly Hispanic patients who were treated in South Los Angeles. Data were collected at diagnosis, 1, 2 and 3 years after. Analyses included z-scores derived from calculated BMIs compared over 3 years per gender, diagnosis, and treatment modality. The unhealthy BMI z-score was defined as >1.04. Results: Thirty-four percent of the predominantly Hispanic sample had unhealthy BMI z-scores of >1.04 correlating to at or greater than the 85th percentile for age and gender. The study cohort’s BMI z-scores significantly increased from 0.15 to 1.29 at year 3 (P < 0.0001), putting 55% of this population in the unhealthy category. Median BMI z-score significantly increased to the unhealthy category at 3 years. Conclusions: Due to the predominance of Hispanic patients in this group, culturally sensitive interventions beginning at diagnosis should be considered.
Pediatric Cancer Survivorship Obesity Health Disparities Long-Term Side Effects Hispanic Health1. Introduction
Around 15,000 children are diagnosed with cancer each year in the United States [1]. As a result of improved treatment modalities, more than 80% of pediatric cancer patients survive. There are currently over 500,000 pediatric cancer survivors in the United States [2]. However, survival can come with long-term sequelae. Two-thirds of childhood cancer survivors face health and psychological challenges as they age and 25% of those are severe, life-threatening and chronic health conditions. Obesity is the most commonly occurring morbidity amongst childhood cancer survivors [3].
Obesity is a significant health issue among the general population. The most recent prevalence of obesity was 18% in children 6 - 11 years and 20.6% in adolescents [4]. Hispanics (25.8%) and non-Hispanic blacks (22%) have been known to have a higher prevalence of obesity compared to non-Hispanic whites [4]. In South Los Angeles, the overall rate of obesity increased by 36% between 1997 and 2011, with a disproportionately higher increase (99%) seen among Hispanics [5].
Among pediatric oncology patients, obesity has been associated with increased risk of treatment toxicity [6]. Subsequently, individuals with higher body mass indexes (BMI) at time of diagnosis have worse outcomes [7] [8] [9] [10]. Orgel et al. report an association between obesity at diagnosis and persistent disease at end of induction as an important predictor of relapse [10]. Furthermore, there are studies that suggest that nutrients provided by adipocytes allow leukemic cells to thrive [11]. Adipose tissue can also alter the pharmacokinetics of chemotherapy medications [11]. Recent studies among survivors of Acute Lymphoblastic Leukemia (ALL) have reported the percentage of overweight and obese patients to be roughly 13% and 16%, respectively [12] [13].
The influence of chemotherapy and radiation on development of obesity has also been evaluated utilizing data from the Childhood Cancer Survivor Study; the largest national cohort of long-term survivors of childhood cancer. Hispanic race/ethnicity has been associated with an increased risk of obesity among male pediatric cancer survivors [14]. Because Non-Hispanic blacks and Hispanic children exhibit higher rates of obesity, understanding the impact of a cancer diagnosis and its treatment on BMI holds particular public health relevance. However, in the pediatric cancer survivorship literature [15], there is an underrepresentation of Hispanics.
This study took place at a safety net hospital located in South Los Angeles, that predominantly serves low income patients with state-sponsored health plans or no insurance. The objective was to identify patterns of change of weight status in a local cohort of childhood cancer survivors, most of whom are Hispanic. Our study objectives were to 1) compare BMI z-score changes over a three-year time period from diagnosis and 2) examine patient and treatment factors associated with weight gain over time.
2. Materials and Methods2.1. Study Population
This study was approved by The Lundquist Institute of Biomedical Innovation at Harbor-UCLA Medical Center’s Institutional Review Board. A retrospective chart review of pediatric oncology patients diagnosed and treated at Harbor-UCLA Medical Center between 1993 and 2009 was performed. A long-term childhood cancer survivor database of individuals at least two years post-treatment was used to identify forty-six potential patients to review. Subjects were excluded if they were either diagnosed at another institution or transferred to another institution prior to the third-year interval. Eligibility criteria included a diagnosis of cancer at the age of 18 years or younger and were diagnosed, treated, and received follow-up care for at least 3 years at our institution. Exclusion criteria included patients who relapsed (n = 2) and patients with Down Syndrome (n = 1), as these patients have different growth parameters, and patients not diagnosed at our institution (n = 4), for an analytical sample of 38 patients.
2.2. Procedures
Data was abstracted using a medical abstraction form by two trained research assistants from paper charts. Data was collected on age, gender, diagnosis, height, and weight, at the following time points: time of diagnosis, 1, 2 and 3 years post diagnosis +/− 2 months.
2.3. Outcome MeasureBody Mass Index Z-Score
Using a data abstraction form, patient’s weights and heights were collected from medical records. BMI was then calculated using the BMI formula: weight (kg)/height (meters)2. BMI z-scores were then determined for each patient using height, weight, sex and age data based on the Centers for Disease Control and Prevention National Center for Health statistics growth curves. Z-score data for age 20 years and older was used for individuals over the age of 20 years. A z-score > 0 represented a higher than average BMI [16].
Per BMI literature, BMI z-scores are considered more accurate than BMI percentiles as it accounts for adiposity in children across ages and gender [13] [17] [18] [19]. Z-scores are classified in the following manner: normal weight (z-score < 1.04), overweight (z-score > 1.04 - 1.64) and obesity (z-score > 1.64) [10] [13] [16] [19] [20] [21]. For our study BMI z-scores were dichotomized into weight status: 1) z-score < 1.04 classified as normal healthy, 2) z-score > 1.04 classified as “unhealthy” as it represented the BMI at 85th percentile and greater [21] [22].
2.4. Main Predictors
Patient’s information abstracted from medical records included: age at diagnosis, cancer type, sex, ethnicity, and treatments received. Cancer types were dichotomized as solid tumors (Langerhan cell histiocytosis (LCH) n = 2, rhabdomyoscarcoma n = 1, pancreatoblastoma n = 1, Wilms tumor n = 5, hepatoblastoma n = 1, neuroblastoma n = 1 and osteosarcoma n = 1) and hematologic malignancies (acute lymphoblastic leukemia n = 18, Hodgkin lymphoma n = 4 and Non-Hodgkin lymphoma n = 1).
2.5. Data Analysis
Descriptive statistics (counts and proportions for categorical data) were generated to describe patient characteristics. Median BMI z-scores and weight statuses (normal versus unhealthy) were reported by patient and treatment characteristics per time period. We examined changes in weight status over a 3-year period and its association with patient and treatment characteristics using generalized linear models (population average) for longitudinal data. The generalized estimating equation (GEE) procedure with an autoregressive working correlation was used to estimate parameters. Generalized estimating equation (GEE) procedures with autoregressive correlation for linear and logistic regression were used to model BMI z-scores and unhealthy weight status, respectively, over time. We reported linear regression with beta coefficients, while logistic regression output is reported in odds ratios. Simple comparisons of BMI z-scores over time were analyzed with paired t-tests. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).
3. Results3.1. Patient Characteristics
Thirty-eight patients met inclusion criteria and had follow up data 36-months from diagnosis. Table 1 shows the patient’s characteristics. There was an equal distribution of males and females in the sample. The majority of patients were Hispanic white (89%) and the median age at diagnosis was 4.2 years. Twenty-six patients had a hematologic malignancy (62%), while twenty-two (64%) received chemotherapy alone and eleven received chemotherapy plus another treatment such as surgery and/or radiation. Of the nine patients that had radiation, three had cranial radiation.
3.1.1. Characteristics by Weight Status at Time of Diagnosis and Time Intervals
Thirty-four percent of the sample was at an unhealthy weight status at diagnosis and 55% at the 3rd year interval. Figure 1 shows the distribution of median BMI z-scores by time periods from diagnosis among varying patient characteristics. Overall, the total study population BMI z-scores increased from 0.15 at baseline to 1.29 at year 3, representing one standard deviation increase in BMI above the average, and putting the population in the unhealthy category (Figure 1(A)). Figure 1(A) shows that the greatest increase in median BMI z-scores occurred between baseline and year 1 (0.15 at baseline to 0.71 at year 1). The median BMI z-score became unhealthy for the total population starting at year 2 (1.07). At baseline, females and males had a similar distribution of normal weight status
Characteristics of a cohort of 38 childhood cancer survivors
Characteristics of study cohort (N = 38)
Total Population
Characteristics
n
%
Sex
Male
19
50.0
Female
19
50.0
Race/Ethnicity
Hispanic
34
89.5
Non-Hispanic White
4
10.5
Cancer diagnosis
Hematologic malignancies
26
68.4
Solid tumors
12
31.6
Treatment Type
Chemotherapy only
22
57.9
Chemotherapy plus other therapy
16
42.1
Age at diagnosis
Median
(min - max)
Overall
4.25
(2 - 17)
Male
4.5
(2 - 17)
Female
4
(2 - 17)
(68% vs. 63%) and a similar median BMI z-score (Figure 1(B)). Individuals with hematologic malignancies at the third-year interval had 69% in the unhealthy weight category with a median z-score of 1.6, which is very close to obesity (correlated z-score of 1.64). Individuals with a hematologic malignancy started off with higher median BMI z-scores than individuals with a solid tumor diagnosis (Figure 1(C)). In regards to the treatment category, 64% of individuals that received chemotherapy only were at an unhealthy weight at the 3rd year interval (median z-score of 1.4, greater than the 90th percentile).
3.1.2. Association between Time from Diagnosis and Patient Characteristics with BMI Z-Score and Unhealthy Weight Status
There was a significant difference in BMI z-scores from baseline (0.26 ± 1.23, Mean ± SD) and year 3 (1 ± 1.01, Mean ± SD); P < 0.0001 in the paired t-test analysis. In the adjusted model, we found that the BMI Z-score increased over a 3-year period (B = 0.74; 95% CI: 0.42, 1.06; P < 0.001) indicating that these patients, who were on average at normal BMI-for-age at diagnosis, were at possible risk for being overweight (Table 2). Patients with hematologic malignancy had average BMI z-scores that were 0.83 points higher than patients with solid tumors (Beta = 0.83, P = 0.012). Sex and treatment type were not significant predictors.
Multivariable regression model for BMI z-scores from time of diagnosis
Associations between time from diagnosis and patient characteristics with BMI Z score (N = 38)
BMI Z score
Estimate
Standard Error
95% CI
P-value
Time
Baseline
Ref.
Year 1
0.33
0.17
0.00, 0.67
0.050
Year 2
0.66
0.17
0.34, 0.99
<0.0001
Year 3
0.74
0.16
0.42, 1.06
<0.0001
Sex
Male
Ref.
Female
−0.07
0.30
−0.65, 0.51
0.821
Cancer diagnosis
Hematologic malignancies
Ref.
Solid tumors
0.83
0.33
0.18, 1.48
0.012
Treatment Type
Chemotherapy only
Ref.
Chemotherapy plus other therapy
−0.13
0.28
0.68, 0.43
0.651
**adjusted for sex, hematological malignancy and treatment with chemotherapy only.
4. Discussion
Obesity is a known complication of treatment for pediatric cancer patients, especially those undergoing treatment for ALL [3] [6]. Obesity has also been known to increase morbidity and mortality among pediatric patients [7] [8]. Hispanic adolescents and children have been known to have a higher prevalence of obesity [23]. This study’s data adds to the current literature that 1) our South Los Angeles cohort BMI z-score increased and changed weight status into the unhealthy category after childhood cancer treatment, 2) that it occurred within the first three years of diagnosis, and 3) that this observation was made in a predominantly Hispanic cohort.
In our study we observed a slightly higher percentage (34%) of individuals at an unhealthy weight status at time of diagnosis, as has been previously reported for a sample of Hispanic ALL pediatric cancer survivors [21]. The median BMI z-score became unhealthy for the total population starting at year 2, with a similar distribution being seen among patients with hematologic malignancies. This could be attributed to corticosteroids used in the treatment of pediatric leukemias, as our sample consisted of more individuals with hematologic malignancies. Also, patients at this point tend to be in the maintenance cycle with a less aggressive regimen and potentially with better appetite and less mucositis or gastrointestinal complications from chemotherapy. In our sample of patients, males had their greatest median BMI z-score at 2 years from diagnosis, and it decreased slightly to 1.47 at 3 years from diagnosis, while females had a 1.05 median BMI z-score that steadily increased to 1.20 at 3 years from diagnosis. As compared to prior reported data, our cohort of females showed a gradual increase in median BMI z-score throughout the intervals, as opposed to decreasing at the 3-year interval. Which at this point, most females would be off-therapy. This increase is also appreciated when comparing trends of overweight/obesity among Hispanic females in the United States during this time period [24]. Overweight/obesity trends overall fluctuated among the general population during this time period [24].
The relationship between obesity and outcomes in ALL has demonstrated inconsistent results. Some studies have linked obesity and poor outcomes among patients with ALL [6] [25]. Some investigators have noted an association between obesity and an increased risk of relapse, as well as increased adverse events throughout treatment [11]. Obese patients are more likely to require treatment for hypertension and hyperglycemia while undergoing therapy. Obesity has also been shown to be a risk factor for neutropenic fever [26], and other toxicities that may prolong treatment or result in standard treatment modifications. Thus, targeted interventions for this population are needed. Overall, our data suggests a need for a targeted intervention for all pediatric cancer patients presumably at the first year of therapy.
Our cohort consists of a majority of Hispanic patients treated at a publicly funded urban hospital. With this data we introduce a different population than has previously been studied [13] [27] [28] [29] [30]. Literature assessing overweight/obesity rates among predominantly Hispanic pediatric cancer patients is sparse. Prior studies assessing BMI changes in this population consisted of patients that were treated at children’s hospitals and had minimal representation of Hispanic patients [13] [27] [28] [29]. Studies have demonstrated that nearly half of Hispanic pediatric cancer survivors are more likely to be overweight and obese at time of follow up [14] [30]. This is not surprising given that recent CDC National Center for Health Statistics (NCHS) data brief reports Hispanic and non-Hispanic blacks have the highest prevalence of obesity compared to the general population, 25.8% and 22.0% [4]. Furthermore, the early childhood longitudinal study demonstrated an inverse relationship between SES and an unhealthy weight among Hispanic children [31]. Socioeconomical conditions have been shown to affect lifestyle choices and access to appropriate health care [32]. These conditions can include living in neighborhood with poor accessibility to healthy food, dangerous neighborhood environments that affect walkability, low income and low education [33]. At baseline minorities have a higher incidence of diabetes and cardiovascular disease [32].
Data suggests that a family-centered, culturally tailored intervention can improve BMI among children [34]. Systematic reviews have shown that a multicomponent, culturally appropriate intervention can help in reducing health disparities. This is not surprising given literature that has shown that “familism” is important among Hispanic families and thus family plays a significant role in the patient’s life and decision making [35].
Limitations should be noted when considering the results of our study. Although there appears to be a significant association of BMI and time, the sample was relatively small and the study may benefit from a larger sample with more robust estimates. Furthermore, certain comparisons may be non-significant due to the underpowered sample. Other potential limitations include the retrospective study design in which a convenience sample was used. These and data from a single institution can affect the generalizability of our results. Our study also assesses individuals with solid tumors as well. A strength, however, is the availability of multiple data points over time per patient.
In addition to diet, exercise, and other lifestyle factors that may affect obesity, further studies should consider assessment of genetic risk factors. For example, neuronal function genes may alter the risk of obesity [36], and can be explored after radiotherapy in childhood cancer survivors.
5. Conclusion
Our sample of predominantly Hispanic pediatric oncology patients had overall healthy weight status at diagnosis but showed an increase in median BMI z-score 3 years from cancer diagnosis—change that resulted in a greater number of subjects in the overweight/obese category. Thus, more studies are needed to assess culturally sensitive interventions starting from the time of cancer diagnosis and its effect on BMI among this population.
Acknowledgements
The authors would like to thank Michelle Doose, PhD, MPH for her assistance with interpretation of some of the statistics and her assistance with manuscript preparation.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Conflicts of Interest
The author(s) indicated no conflict of interest.
Cite this paper
Morales, S., Gotesman, M., Su, E.M., Yee, J.K., Ruiz, M.E., Friedlander, S., Lasky III, J.L. and Panosyan, E.H. (2022) Increasing BMI Z-Scores 3 Years after Diagnosis among a Multiethnic Cohort of Childhood Cancer Survivors Treated in South Los Angeles. Journal of Biosciences and Medicines, 10, 141-151. https://doi.org/10.4236/jbm.2022.103015
NOTESReferencesSiegel, R.L., Miller, K.D. and Jemal, A. (2018) Cancer Statistics, 2018. A Cancer Journal for Clinicians, 68, 7-30. https://doi.org/10.3322/caac.21442Miller, K.D., Siegel, R.L., Lin, C.C., et al. (2016) Cancer Treatment and Survivorship Statistics, 2016. A Cancer Journal for Clinicians, 66, 271-289.https://doi.org/10.3322/caac.21349Hudson, M.M., Ness, K.K., Gurney, J.G., et al. (2013) Clinical Ascertainment of Health Outcomes among Adults Treated for Childhood Cancer. Journal of the American Medical Association, 309, 2371-2381.https://doi.org/10.1001/jama.2013.6296Hales, C.M., Carroll, M.D., Fryar, C.D. and Ogden, C.L. (2017) Prevalence of Obesity among Adults and Youth: United States, 2015-2016. National Center for Health Statistics: Data Brief, No. 288, 1-8.LA Health (2012) Trends in Obesity: Adult Obesity Continues to Rise.Orgel. E., Sposto, R., Malvar, J., et al. (2014) Impact on Survival and Toxicity by Duration of Weight Extremes during Treatment for Pediatric Acute Lymphoblastic Leukemia: A Report from the Children’s Oncology Group. Journal of Clinical Oncology, 32, 1331-1337. https://doi.org/10.1200/JCO.2013.52.6962Inaba, H., Surprise, H.C., Pounds, S., et al. (2012) Effect of Body Mass Index on the Outcome of Children with Acute Myeloid Leukemia. Cancer, 118, 5989-5996.https://doi.org/10.1002/cncr.27640Lange, B.J., Gerbing, R.B., Feusner, J., et al. (2005) Mortality in Overweight and Underweight Children with Acute Myeloid Leukemia. Journal of the American Medical Association, 93, 203-211. https://doi.org/10.1001/jama.293.2.203Gelelete, C.B., Pereira, S.H., Azevedo, A.M., et al. (2011) Overweight as a Prognostic Factor in Children with Acute Lymphoblastic Leukemia. Obesity (Silver Spring), 19, 1908-1911. https://doi.org/10.1038/oby.2011.195Orgel, E., Tucci, J., Alhushki, W., et al. (2014) Obesity Is Associated with Residual Leukemia following Induction Therapy for Childhood B-Precursor Acute Lymphoblastic Leukemia. Blood, 124, 3932-3938.https://doi.org/10.1182/blood-2014-08-595389Sheng, X. and Mittelman, S.D. (2014) The Role of Adipose Tissue and Obesity in Causing Treatment Resistance of Acute Lymphoblastic Leukemia. Frontiers in Pediatrics, 2, Article 53. https://doi.org/10.3389/fped.2014.00053Eissa, H.M., Zhou, Y., Panetta, J.C., et al. (2017) The Effect of Body Mass Index at Diagnosis on Clinical Outcome in Children with Newly Diagnosed Acute Lymphoblastic Leukemia. Blood Cancer Journal, 7, e531.https://doi.org/10.1038/bcj.2017.11Withycombe, J.S., Smith, L.M., Meza, J.L., et al. (2015) Weight Change during Childhood Acute Lymphoblastic Leukemia Induction Therapy Predicts Obesity: A Report from the Children’s Oncology Group. Pediatric Blood and Cancer, 62, 434-439. https://doi.org/10.1002/pbc.25316Green, D.M., Cox, C.L., Zhu, L., et al. (2012) Risk Factors for Obesity in Adult Survivors of Childhood Cancer: A Report from the Childhood Cancer Survivor Study. Journal of Clinical Oncology, 30, 246-255.https://doi.org/10.1200/JCO.2010.34.4267Castellino, S.M., Casillas, J., Hudson, M.M., et al. (2005) Minority Adult Survivors of Childhood Cancer: A Comparison of Long-Term Outcomes, Health Care Utilization, and Health-Related Behaviors from the Childhood Cancer Survivor Study. Journal of Clinical Oncology, 23, 6499-6507.https://doi.org/10.1200/JCO.2005.11.098Zhang, F.F., Kelly, M.J., Saltzman, E., Must, A., Roberts, S.B. and Parsons, S.K. (2014) Obesity in Pediatric ALL Survivors: A Meta-Analysis. Pediatrics, 133, e704-e715. https://doi.org/10.1542/peds.2013-3332Field, A.E., Laird, N., Steinberg, E., Fallon, E., Semega-Janneh, M. and Yanovski, J.A. (2003) Which Metric of Relative Weight Best Captures Body Fatness in Children? Obesity Research, 11, 1345-1352. https://doi.org/10.1038/oby.2003.182Must, A. and Anderson, S.E. (2006) Body Mass Index in Children and Adolescents: Considerations for Population-Based Applications. International Journal of Obesity, 30, 590-594. https://doi.org/10.1038/sj.ijo.0803300Wang, Y. and C, H.J. (2012) Use of Percentiles and Z-Scores in Anthropometry. In: Victor, R.P., Ed., Handbook of Anthropometry: Physical Measures of Human Form in Health and Disease, Springer, New York, 29-48.https://doi.org/10.1007/978-1-4419-1788-1_2Zhang, F.F. and Parsons, S.K. (2015) Obesity in Childhood Cancer Survivors: Call for Early Weight Management. Advances in Nutrition, 6, 611-619.https://doi.org/10.3945/an.115.008946Baillargeon, J., Langevin, A.M., Lewis, M., et al. (2005) Therapy-Related Changes in Body Size in Hispanic Children with Acute Lymphoblastic Leukemia. Cancer, 103, 1725-1729. https://doi.org/10.1002/cncr.20948Himes, J.H. and Dietz, W.H. (1994) Guidelines for Overweight in Adolescent Preventive Services: Recommendations from an Expert Committee. The Expert Committee on Clinical Guidelines for Overweight in Adolescent Preventive Services. The American Journal of Clinical Nutrition, 59, 307-316.https://doi.org/10.1093/ajcn/59.2.307Skinner, A.C., Ravanbakht, S.N., Skelton, J.A., Perrin, E.M. and Armstrong, S.C. (2018) Prevalence of Obesity and Severe Obesity in US Children, 1999-2016. Pediatrics, 141, e20173459.Skinner, A.C., Perrin, E.M. and Skelton, J.A. (2016) Prevalence of Obesity and Severe Obesity in US children, 1999-2014. Obesity (Silver Spring), 24, 1116-1123.https://doi.org/10.1002/oby.21497Orgel, E., Genkinger, J.M., Aggarwal, D., Sung, L., Nieder, M. and Ladas, E.J. (2016) Association of Body Mass Index and Survival in Pediatric Leukemia: A Meta-Analysis. The American Journal of Clinical Nutrition, 103, 808-817.https://doi.org/10.3945/ajcn.115.124586Meenan, C.K., Kelly, J.A., Wang, L., Ritchey, A.K. and Maurer, S.H. (2019) Obesity in Pediatric Patients with Acute Lymphoblastic Leukemia Increases the Risk of Adverse Events during Pre-Maintenance Chemotherapy. Pediatric Blood & Cancer, 66, e27515. https://doi.org/10.1002/pbc.27515Lindemulder, S.J., Stork, L.C., Bostrom, B., et al. (2015) Survivors of Standard Risk Acute Lymphoblastic Leukemia Do not Have Increased Risk for Overweight and Obesity Compared to Non-Cancer Peers: A Report from the Children’s Oncology Group. Pediatric Blood & Cancer, 62, 1035-1041. https://doi.org/10.1002/pbc.25411Garmey, E.G., Liu, Q., Sklar, C.A., et al. (2008) Longitudinal Changes in Obesity and Body Mass Index among Adult Survivors of Childhood Acute Lymphoblastic Leukemia: A Report from the Childhood Cancer Survivor Study. Journal of Clinical Oncology, 26, 4639-4645. https://doi.org/10.1200/JCO.2008.16.3527Oeffinger, K.C., Mertens, A.C., Sklar, C.A., et al. (2003) Obesity in Adult Survivors of Childhood Acute Lymphoblastic Leukemia: A Report from the Childhood Cancer Survivor Study. Journal of Clinical Oncology, 21, 1359-1365.https://doi.org/10.1200/JCO.2003.06.131Brown, A.L., Lupo, P.J., Danysh, H.E., Okcu, M.F., Scheurer, M.E. and Kamdar, K.Y. (2016) Prevalence and Predictors of Overweight and Obesity among a Multiethnic Population of Pediatric Acute Lymphoblastic Leukemia Survivors: A Cross-Sectional Assessment. Journal of Pediatric Hematology/Oncology, 38, 429-436. https://doi.org/10.1097/MPH.0000000000000555Jones-Smith, J.C., Dieckmann, M.G., Gottlieb, L., Chow, J. and Fernald, L.C. (2014) Socioeconomic Status and Trajectory of Overweight from Birth to Mid-Childhood: the Early Childhood Longitudinal Study-Birth Cohort. PLoS ONE, 9, e100181. https://doi.org/10.1371/journal.pone.0100181Stolley, M.R., Sharp, L.K., Tangney, C.C., et al. (2015) Health Behaviors of Minority Childhood Cancer Survivors. Cancer, 121, 1671-1680.https://doi.org/10.1002/cncr.29202Braveman, P. (2014) What Is Health Equity: And How Does a Life-Course Approach Take Us Further toward It? Maternal and Child Health Journal, 18, 366-372.https://doi.org/10.1007/s10995-013-1226-9Falbe, J., Cadiz, A.A., Tantoco, N.K., Thompson, H.R. and Madsen, K.A. (2015) Active and Healthy Families: A Randomized Controlled Trial of a Culturally Tailored Obesity Intervention for Latino Children. Academic Pediatrics, 15, 386-395.https://doi.org/10.1016/j.acap.2015.02.004Halgunseth, L.C., Ispa, J.M. and Rudy, D. (2006) Parental Control in Latino Families: An Integrated Review of the Literature. Child Development, 77, 1282-1297.https://doi.org/10.1111/j.1467-8624.2006.00934.xTimshel, P.N., Thompson, J.J. and Pers, T.H. (2020) Genetic Mapping of Etiologic Brain Cell Types for Obesity. eLife, 9, e55851.https://doi.org/10.7554/eLife.55851