OBJECTIVES

To assess the prevalence of overweight or obesity among children with medical complexity (CMC), compared with children without medical complexity, and explore potentially modifiable mechanisms.

METHODS

This study involved a retrospective cohort of 41 905 children ages 2 to 18 seen in 2019 at a single academic medical center. The primary outcome was overweight or obesity, defined as a body mass index of ≥85% for age and sex. CMC was defined as ≥1 serious chronic condition in ≥1 system. Obesogenic conditions and medications were defined as those typically associated with excess weight gain. Multivariable logistic regression was used to adjust for common confounders.

RESULTS

Of the children in the cohort, 29.5% were CMC. Overweight or obesity prevalence was higher among CMC than non-CMC (31.9% vs 18.4%, P ≤.001, adjusted odds ratio [aOR] 1.27, 95% confidence interval [CI] 1.20–1.35). Among CMC, the risk for overweight or obesity was higher among children with metabolic conditions (aOR 2.09, 95% CI 1.88–2.32), gastrointestinal conditions (aOR 1.23 95% CI 1.06–1.41), malignancies (aOR 1.21 95% CI 1.07–1.38), and Spanish-speaking parents (aOR 1.47 95% CI 1.30–1.67). Among overweight or obese CMC, 91.6% had no obesogenic conditions, and only 8.5% had been seen by a registered dietitian in the previous year.

CONCLUSIONS

CMC are significantly more likely to be overweight or obese when compared with children without medical complexity. Although many CMC cases of overweight appear to be preventable, further research is necessary to determine if and how to prevent comorbid obesity among CMC.

What’s Known on This Subject:

Children with medical complexity (CMC) account for a disproportionate share of child health care use, and 1 in 3 US children is overweight or obese. No previous studies have examined the prevalence of overweight and obesity among CMC.

What This Study Adds:

We found that CMC are 27% more likely to be overweight or obese when compared with children without chronic medical conditions. Most cases of CMC obesity may be preventable because they are not associated with obesogenic conditions.

A child with medical complexity (CMC) is characterized as having underlying chronic health conditions, significant functional status limitations, and increased hospitalization risk.1  CMC represent 1% to 11% of all US children but account for >30% of total pediatric health care costs.2,3  One hypothesized preventable comorbidity for CMC is overweight and obesity.4 

In fact, obesity may confer a “double disadvantage” for children with serious chronic conditions. Affecting >1 in 3 US children, obesity has been identified as a preventable public health problem across the life course, a significant cause of impaired quality of life and decreased lifespan,57  and a source of health inequities.810  Among CMC, obesity may be an important and preventable cause of comorbidity, including delays or complications to life-saving medical procedures (eg, cardiac surgery, spinal fusion),11  limitations to functional status (eg, mobility impairment),12  increased risk of infection (eg, urinary tract infections),13  and increased risk of acquired chronic conditions (eg, poor bone health, liver disease, diabetes, cardiovascular disease).14  Many CMC are immobile, dependent on feeding tubes, and/or reliant on supplemental nutrition,15,16,17,18,19  which may place them at risk for overfeeding.20  Although many cases of obesity among CMC may be preventable, some CMC may have nonpreventable causes of obesity, especially if they are due to obesogenic conditions (eg, Prader Willi Syndrome), for which obesity is a typical manifestation, or “obesogenic medications” (eg, corticosteroids), which predispose to excess weight gain. No previous study, however, has documented the prevalence or potential mechanisms of obesity among CMC.

To address the gap, we aimed (1) to identify the relative prevalence of overweight or obesity among CMC, compared with children without chronic medical conditions, and (2) to explore potentially preventable mechanisms for overweight or obesity among CMC. We hypothesized both that the prevalence of overweight and obesity would be higher among CMC than among children without chronic medical conditions and that neurologic impairment and feeding tubes would be the factors most associated with increased obesity prevalence among CMC.

We conducted a retrospective cohort study of children aged 2 to 18 years, seen between January 1, 2019 and December 31, 2019 in a single health system. The study was approved by the Stanford University Human Subjects Research Institutional Review Board.

We extracted patient-specific data for children during this time period from the Stanford Medicine Research Data Repository, a clinical data warehouse containing Epic data from Stanford Health Care, the Stanford Children’s Hospital, the University Healthcare Alliance, and Packard Children’s Health Alliance clinics, including auxiliary data from nonprovider-dependent hospital services, such as radiology and clinical laboratory.21  Eligible subjects were children aged ≥2 years and ≤18 years for whom there was a valid weight and height recorded for at least 1 clinical encounter (inpatient or outpatient) on a single date. We excluded children who did not have a valid weight and height registered during the same visit, those whose age was <2 years or >18 years, and those who had “metabolic syndrome” as their qualifying serious chronic condition. (See Fig 1 for CONSORT diagram)

The primary outcome was child overweight or obesity, defined as body mass index (BMI) ≥85th percentile, adjusted for age and sex, by the Centers for Disease Control and Prevention (CDC) standardized growth charts. Because condition-specific growth charts are subject to bias and do not offer BMI percentile estimates, we used the CDC BMI-for-age growth charts for all children, regardless of medical condition, in line with CDC recommendations and previous research standards.22,23  For each child, we calculated the BMI (kg/m2) using the weight (kg), height (m), age (years), and sex from the first date during the study period at which both weight and height were recorded.15  If >1 weight and height was available during the study period, we used the earliest valid record of coincident weight and height. Using the CDC charts of BMI percentile, standardized by age and sex, we defined underweight as BMI <5th percentile, normal weight as BMI 5th to 84th percentile, overweight as 85th to 94th percentile, and obese as ≥95th percentile.4  For each child categorized as obese, we further subcategorized their weight status into 1 of 3 obesity classes on the basis of the percent above the child’s age- and sex-adjusted 95th percentile body mass index (BMI95): Class I (<120% of BMI95), Class II (BMI 120% to 139% of BMI95) or Class III (≥140% of BMI95).24,25 

The primary independent variable was CMC, using the schema proposed by Feudtner.26  The Feudtner schema applies International Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes to identify a child as a CMC if she or he has a serious chronic condition, defined as “any medical condition that can be reasonably expected to last at least 12 months (unless death intervenes) and to involve either several different organ systems or 1 organ system severely enough to require specialty pediatric care and probably some period of hospitalization in a tertiary care center”27 . This schema further subcategorizes each CMC into 1 of 12 CMC subgroups: congenital and genetic conditions, neurologic and neuromuscular conditions, gastrointestinal conditions, cardiovascular conditions, respiratory conditions, renal and urologic conditions, hematologic and immunologic conditions, metabolic conditions, malignancy conditions, neonatal and prematurity conditions, technology dependance, and transplant recipients. Subgroups were not mutually exclusive. For example, a child with severe pulmonary disease and malabsorption from cystic fibrosis would be categorized as CMC and subcategorized in at least 2 CMC subgroups (respiratory conditions and gastrointestinal conditions). Examples of children classified as non-CMC include otherwise healthy children or those with noncomplex chronic conditions, such as asthma without comorbidity.

“Obesogenic medical condition” was defined as a chronic medical condition that typically causes obesity or presents with obesity as a primary manifestation; based on previous literature, this includes Cushing disease, hypothyroidism, growth hormone deficiency, hypopituitarism, Prader Willi, and Albright hereditary osteodystrophy2831  (see Appendix 1 in Supplemental Information for a complete list of IDC-10 codes for each condition). “Obesogenic medication” was defined as a medication that is associated with increased weight gain with chronic use; this includes corticosteroids, antiepileptics, antidepressants, and antipsychotics3235  (see Appendix 2 in Supplemental Information for a complete list medications). For this study, we defined chronic use as ≥2 pharmacy refills.

The final models included sociodemographic characteristics known to be associated with obesity risk (age, sex, ethnicity/race, language, insurance type), medical characteristics known to be associated with illness severity (supplemental nutrition, technology dependence), and potential mediators and moderators (parent preferred language, obesogenic medical conditions, obesogenic medications, registered dietitian consultation). “Supplemental nutrition”36  was defined as any documentation of the use of a feeding tube by ICD-10 and current procedural terminology coding (CPT) during the study period37  (see Appendix 3 in Supplemental Information for a list of ICD-10 codes and CPT codes). “Registered dietitian visit” was defined as any CPT code for a registered dietitian service during the study period38  (see Appendix 4 in Supplemental information for a list of CPT codes). All independent variables were derived from the electronic health record.

We used descriptive statistics to assess for differences in mean age (standard deviation) and relative frequencies for sociodemographic characteristics, weight status (BMI percentile categories), presence of obesogenic medical conditions, use of obesogenic treatments, and CMC subgroups for the CMC group. We used t tests to compare means and χ2 statistical test to compare frequencies. A binary logistic regression model was used to calculate adjusted odds ratios for a single outcome: child overweight and obesity. Independent variables for this model included medical complexity (CMC vs children without medical complexity), child age, child sex, child race/ethnicity, parent preferred language, and child insurance type. A second model was developed to assess the independent contribution of CMC characteristics, including medical subgroups, the presence of enteral feeding tubes, and technology dependence. We then fit separate models that explored effect modification from conditions or treatments that potentiate obesity. For a sensitivity analysis, we considered 2 more restrictive definitions of “medical complexity.” To do so, we repeated the analyses above for each of 2 alternative definitions:1  children with serious chronic conditions in 2 or more organ systems and2  children with serious chronic conditions in 3 or more organ systems. Data were stored securely at Stanford’s protected servers. SAS software was used for data management and data analysis.

Of 41 905 children in the cohort, 29.5% were identified as CMC on the basis of the above criteria (see Table 1 for sociodemographic characteristics of children by group). Obesogenic medical conditions were present in 72 (0.2%) children without medical complexity and 759 (6.1%) CMC. Obesogenic medication use was found among 8.9% of CMC. Among CMC, a registered dietitian visit was recorded for 335 children who were overweight or obese (8.5%) and for 76 children who were underweight (12.7%).

Compared with children with no chronic medical conditions, CMC were more likely to be overweight (14.5% vs 11%, P ≤ .001) or obese (17.4% vs 7.3%, P ≤ .001). CMC, compared with non-CMC, also had a higher prevalence of Class I obesity (11.1% vs 6.2%), Class II obesity (5.7% vs 0.8%), and Class III obesity (0.6% vs 0.3%).

After adjustment for age, sex, insurance type, race and ethnicity, and parent preferred language, overweight or obesity risk remained higher for CMC (adjusted odds ratio [aOR] 1.27, 95% confidence interval [CI] 1.20–1.35). (Table 2) Among CMC, overweight or obese risk was higher for children who were Hispanic (aOR 1.41, 95% CI 1.26–1.57) or Black (aOR 1.46, 95% CI 1.09–1.95), had public insurance coverage (aOR 1.76, 95% CI 1.60–1.94) and whose parents’ preferred language was Spanish (aOR 1.47, 95% CI 1.30–1.67 (see Fig 2 for aORs for all characteristics associated with obesity risk among CMC).

Among the 3934 CMC who were overweight or obese, 3605 (91.6%) had no obesogenic medical conditions. Obesity or overweight risk was greatest among those with metabolic conditions (aOR 2.09, 95% CI 1.88–2.32), gastrointestinal conditions (aOR 1.23, 95% CI 1.06–1.41), and malignancies (aOR 1.21, 95% CI 1.07–1.38). Enteral feeds were associated with a lower risk of obesity or overweight (aOR 0.44, 95% CI 0.34–0.57), as well as being female (aOR 0.86, 95% CI 0.80–0.93).

In sensitivity analyses, these patterns persisted after using 1 of 2 more restrictive definitions of “medical complexity.” When defined as involving ≥2 body systems, CMC remained at increased risk for overweight or obesity, after adjusting for covariates (aOR 1.13, 95% CI 1.03–1.24). When defined as involving ≥3 body systems, however, CMC were not at an increased risk for overweight or obesity (aOR 0.95, 95% CI 0.84–1.07; see Table 3).

This is a large-sample study across a health system to assess overweight and obesity risk among CMC. After adjusting for age, sex, and socioeconomic status, we found that CMC are 27% more likely to be overweight or obese when compared with otherwise healthy children. Among CMC, many cases of obesity and overweight may be preventable because most obese or overweight CMC obesity (91.6%) had no obesogenic condition or medication. In addition, this study suggests the potential for dietary behavioral intervention because most CMC (>90%) had not received a registered dietitian consult in the previous year. Together, these findings suggest potential targets for systems-level interventions designed to reduce preventable comorbidity and address health disparities.

Many factors may contribute to the higher risk of overweight or obesity among CMC. Some factors may be nonpreventable because they are intrinsic to certain chronic illnesses at the levels of biology (eg, obesogenic genetic or epigenetic predisposition) and patient care (eg, obesogenic medications or therapies).20,3134,39,40  Other factors may be preventable because they are subject to parent or provider discretion at the levels of nutrition (eg, supplemental nutrition, feeding tubes) or physical activity (eg, adherence with activity or therapy recommendations).4042  In this study, we tested some of these hypotheses. As expected, obesogenic conditions were associated with increased obesity risk. Counter to our hypothesis, however, obesogenic medications were not associated with increased obesity risk, and feeding tubes were associated with a lower prevalence of obesity and overweight. The latter finding could be explained by the fact that a child with a feeding tube often has a preexisting diagnosis associated with undernutrition or failure to thrive. This study also rejected a previous hypothesis that CMC with neurologic impairment would have a higher obesity risk; if true, we suspect that, despite having limited mobility, these children may have other mitigating risks, such as increased gastrointestinal losses (eg, emesis or regurgitation), increased caloric needs (especially in those with spasticity), and/or oromotor dysfunction.43 

Of note, other factors associated with obesity among CMC in this sample suggest potential avenues for intervention. Only 8.5% of overweight or obese CMC in our sample had been seen by a registered dietitian in the previous year. This suggests a promising area for future study, especially because CMC frequently visit subspecialty care centers that may be more likely than community clinics to be staffed with trained registered dietitians. Also, overweight or obese CMC were most likely to fall into 1 of 3 CMC subgroups: metabolic, gastrointestinal, and malignancy. It remains possible that obesity in these CMC subgroups may remain nonpreventable because it may be due to other biological or iatrogenic factors, such as therapeutic fluid imbalance (eg, liver or kidney disease) or other unmeasured obesogenic conditions or therapy. It is important to note we excluded children with a diagnosis of “Metabolic Syndrome” from the CMC group, given that one of the qualifying criteria for Metabolic Syndrome includes being obese and because its diagnosis in pediatrics is controversial.44 

Finally, as in previous studies of childhood obesity, we found racial and ethnic identity factors to be independently associated with increased obesity risk among CMC, specifically among children identified as Hispanic or non-Hispanic Black ethnicity, and among children of parents who are primarily Spanish-speaking.4548  Although race and ethnicity are social constructs, the findings are strongly influenced by structural and historic inequities in the health of US children, including the transgenerational impact of discriminatory policies in housing, health, and welfare that have impaired minoritized communities’ access to safe environments, physical activity, nutritious food, education, and health care.4952 

The study findings are subject to other limitations and biases common to single-site studies using electronic medical records. Many confounding factors were unmeasured, including biological factors (eg, gestational age), genetic predisposition (eg, parental BMI), behavioral factors (eg, nutrition, physical activity, sleep), and social factors (eg, food and housing insecurity, household composition, public policy). The sample may be nonrepresentative or lack sufficient statistical power to detect certain effect sizes, although we benefited from a higher CMC prevalence than in the general US population (29% vs <5%). Measurement bias is also a concern, given that we were reliant on factors captured in the electronic medical record. For example, the nonassociation between obesogenic medications and obesity risk might have resulted from coding bias because we were not able to account for the frequency or duration of medication use. Because of the retrospective study design, we relied on nonstandardized measurements of weight and height in clinical practice; to mitigate measurement error, we excluded improbable values of weight and height (eg, zero or negative) detected during the cohort creation.

Our results reveal that obesity is a common and potentially preventable comorbidity among CMC and that dietary behavioral interventions may be potential targets for early intervention to prevent weight-related comorbidities for this vulnerable population. Medical subspecialties with CMC at the highest obesity risk (eg, metabolic conditions, gastrointestinal conditions) suggest possible targeted subpopulations. Because their children are at higher risk, parents with limited English proficiency might be respectfully asked and reimbursed to participate as codesigners of these interventions. Further research is needed to better characterize obesity among CMC, as well as to identify potentially modifiable factors. To assess the generalizability of these findings, this should include observational studies in larger, multiinstitutional samples, as well as prospective research to assess the effectiveness of nutritional or behavioral interventions. Given the difficulty in obtaining accurate anthropomorphic measurements in this vulnerable population,53  validation of improved BMI measurement techniques in the medically complex population could be a potential area for future research. Future research might also explore possible CMC as a risk factor for other types of malnutrition, including underweight status. Finally, future studies may also explore the impact of public policies and programs on racial/ethnic disparities in obesity and obesity-related comorbidities among CMC.

This project was funded by the Stanford Maternal and Child Health Research Institute. At the time when this research was conducted, Maria Isabel Peinado Fabregat was an Ernest and Amelia Gallo Endowed Postdoctoral Fellow. We thank the Stanford Medicine Research Information Technology (IT) team and the Stanford School of Medicine Research Office, which codeveloped and operate the STARR platform, from which the data for this study were derived.

Dr Peinado Fabregat contributed to the concept and design of the study, analysis and interpretation of data, and the drafting and revision the manuscript; Mrs Saynina contributed to the analysis and interpretation of data and revision of the manuscript; Dr Sanders supervised Dr Peinado Fabregat and contributed to the concept and design, interpretation of data, and revision of the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: This project was funded by the Stanford Maternal and Child Health Research Institute. At the time when this research was conducted, Dr Peinado Fabregat was an Ernest and Amelia Gallo Endowed Postdoctoral Fellow.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no potential conflicts of interest relevant to this article to disclose.

aOR

adjusted odds ratio

BMI

body mass index

BMI95

age- and sex-adjusted 95th percentile body mass index

CDC

Centers for Disease Control and Prevention

CI

confidence interval

CMC

children with medical complexity

CPT

current procedural terminology

ICD-10

International Classification of Diseases, Tenth Revision

1
Cohen
E
,
Berry
JG
,
Camacho
X
, et al
.
Patterns and costs of health care use of children with medical complexity
.
Pediatrics
.
2012
;
130
(
6
):
e1463
e1470
2
Murphy
NA
,
Clark
EB
.
Children with complex medical conditions: an under-recognized driver of the pediatric cost crisis
.
Curr Treat Options Pediatr
.
2016
;
2
(
4
):
289
295
3
Leyenaar
JK
,
Schaefer
AP
,
Freyleue
SD
, et al
.
Prevalence of children with medical complexity and associations with health care utilization and in-hospital mortality
.
JAMA Pediatr
.
2022
;
176
(
6
):
e220687
4
Centers for Disease Control and Prevention
.
2000 CDC growth charts for the United States
.
Available at: https://www.cdc.gov/growthcharts/cdc_charts.htm. Accessed August 3, 2022
5
Ogden
CL
,
Carroll
MD
,
Lawman
HG
, et al
.
Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014
.
JAMA
.
2016
;
315
(
21
):
2292
2299
6
Huang
JY
,
Qi
SJ
.
Childhood obesity and food intake
.
World J Pediatr
.
2015
;
11
(
2
):
101
107
7
Fryar
C
,
Carroll
M
,
Afful
J
;
Centers for Disease Control and Prevention
;
National Center for Health Statistics
.
Prevalence of overweight, obesity, and severe obesity among children and adolescents aged 2–19 years: United States, 1963–1965 through 2017–2018
.
8
Fryar
CD
,
Carroll
MD
,
Ogden
CL
;
Centers for Disease Control and Prevention
;
National Center for Health Statistics
.
Prevalence of overweight, obesity, and severe obesity among children and adolescents aged 2–19 years: United States, 1963–1965 through 2015–2016
.
9
Skinner
AC
,
Skelton
JA
.
Prevalence and trends in obesity and severe obesity among children in the United States, 1999-2012
.
JAMA Pediatr
.
2014
;
168
(
6
):
561
566
10
Pak-Gorstein
S
,
Haq
A
,
Graham
EA
.
Cultural influences on infant feeding practices
.
Pediatr Rev
.
2009
;
30
(
3
):
e11
e21
11
Patel
N
,
Bagan
B
,
Vadera
S
, et al
.
Obesity and spine surgery: relation to perioperative complications
.
J Neurosurg Spine
.
2007
;
6
(
4
):
291
297
12
Duncan
MJ
,
Stanley
M
,
Leddington Wright
S
.
The association between functional movement and overweight and obesity in British primary school children
.
BMC Sports Sci Med Rehabil
.
2013
;
5
(
1
):
11
13
Grier
WR
,
Kratimenos
P
,
Singh
S
, et al
.
Obesity as a risk factor for urinary tract infection in children
.
Clin Pediatr (Phila)
.
2016
;
55
(
10
):
952
956
14
Sahoo
K
,
Sahoo
B
,
Choudhury
AK
, et al
.
Childhood obesity: causes and consequences
.
J Family Med Prim Care
.
2015
;
4
(
2
):
187
192
15
Ostbye
T
,
Malhotra
R
,
Landerman
LR
.
Body mass trajectories through adulthood: results from the National Longitudinal Survey of Youth 1979 Cohort (1981-2006)
.
Int J Epidemiol
.
2011
;
40
(
1
):
240
250
16
Berry
JG
,
Agrawal
R
,
Kuo
DZ
, et al
.
Characteristics of hospitalizations for patients who use a structured clinical care program for children with medical complexity
.
J Pediatr
.
2011
;
159
(
2
):
284
290
17
American Dietetic Association
.
Providing nutrition services for infants, children, and adults with developmental disabilities and special health care needs
.
J Am Diet Assoc
.
2004
;
104
(
1
):
97
107
18
Boyer
SW
,
Barclay
LJ
,
Burrage
LC
.
Inherited metabolic disorders: aspects of chronic nutrition management
.
Nutr Clin Pract
.
2015
;
30
(
4
):
502
510
19
Fisher
JO
,
Birch
LL
,
Smiciklas-Wright
H
,
Picciano
MF
.
Breast-feeding through the first year predicts maternal control in feeding and subsequent toddler energy intakes
.
J Am Diet Assoc
.
2000
;
100
(
6
):
641
646
20
Marchand
V
,
Motil
KJ
;
NASPGHAN Committee on Nutrition
.
Nutrition support for neurologically impaired children: a clinical report of the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition
.
J Pediatr Gastroenterol Nutr
.
2006
;
43
(
1
):
123
135
21
Stanford University
.
STAnford Research Repository (STARR) tools
.
Available at: https://med.stanford.edu/starr-tools.html. Accessed March 30, 2021
22
Centers for Disease Control and Prevention
.
Growth charts - growth chart frequently asked questions
.
Available at: https://www.cdc.gov/growthcharts/growthchart_faq.htm. Accessed August 4, 2022
23
Centers for Disease Control and Prevention
;
US Department of Health and Human Services
;
Health Resources and Services Administration
;
Maternal and Child Health Bureau
.
The CDC growth charts for children with special health care needs
.
Available at: https://depts.washington.edu/growth/cshcn/text/page6a.htm. Accessed August 3, 2022
24
Skinner
AC
,
Perrin
EM
,
Moss
LA
,
Skelton
JA
.
Cardiometabolic risks and severity of obesity in children and young adults
.
N Engl J Med
.
2015
;
373
(
14
):
1307
1317
25
Skinner
AC
,
Ravanbakht
SN
,
Skelton
JA
, et al
.
Prevalence of obesity and severe obesity in US children, 1999-2016
.
Pediatrics
.
2018
;
141
(
3
):
e20173459
26
Feudtner
C
,
Feinstein
JA
,
Zhong
W
, et al
.
Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation
.
BMC Pediatr
.
2014
;
14
(
1
):
199
27
Feudtner
C
,
Christakis
DA
,
Connell
FA
.
Pediatric deaths attributable to complex chronic conditions: a population-based study of Washington State, 1980-1997
.
Pediatrics
.
2000
;
106
(
1 Pt 2
Supplement_1
):
205
209
28
Geets
E
,
Meuwissen
MEC
,
Van Hul
W
.
Clinical, molecular genetics and therapeutic aspects of syndromic obesity
.
Clin Genet
.
2019
;
95
(
1
):
23
40
29
Vos
N
,
Oussaada
SM
,
Cooiman
MI
, et al
.
Bariatric surgery for monogenic non-syndromic and syndromic obesity disorders
.
Curr Diab Rep
.
2020
;
20
(
9
):
44
30
Kehinde
TA
,
Bhatia
A
,
Olarewaju
B
, et al
.
Syndromic obesity with neurodevelopmental delay: opportunities for targeted interventions
.
Eur J Med Genet
.
2022
;
65
(
3
):
104443
31
Chesi
A
,
Grant
SFA
.
The genetics of pediatric obesity
.
Trends Endocrinol Metab
.
2015
;
26
(
12
):
711
721
32
Sweeney
B
,
Kelly
AS
,
San Giovanni
CB
, et al
.
Clinical approaches to minimize iatrogenic weight gain in children and adolescents
.
Clin Obes
.
2021
;
11
(
1
):
e12417
33
Turer
CB
,
Barlow
SE
,
Sarwer
DB
, et al
.
Association of clinician behaviors and weight change in school-aged children
.
Am J Prev Med
.
2019
;
57
(
3
):
384
393
34
Feinstein
JA
,
Hall
M
,
Antoon
JW
, et al
.
Chronic medication use in children insured by Medicaid: a multistate retrospective cohort study
.
Pediatrics
.
2019
;
143
(
4
):
e20183397
35
Tsai
AG
,
Wadden
TA
.
In the clinic: obesity
.
Ann Intern Med
.
2013
;
159
(
5
):
ITC3
ITC1
,
ITC3
ITC15
,
quiz ITC3–ITC16
36
Cederholm
T
,
Barazzoni
R
,
Austin
P
, et al
.
ESPEN guidelines on definitions and terminology of clinical nutrition
.
Clin Nutr
.
2017
;
36
(
1
):
49
64
37
Lin
JL
,
Rigdon
J
,
Van Haren
K
, et al
.
Gastrostomy tubes placed in children with neurologic impairment: associated morbidity and mortality
.
J Child Neurol
.
2021
;
36
(
9
):
727
734
38
Parrott
JS
,
White
JV
,
Schofield
M
, et al
.
Current coding practices and patterns of code use of registered dietitian nutritionists: the Academy of Nutrition and Dietetics 2013 coding survey
.
J Acad Nutr Diet
.
2014
;
114
(
10
):
1619
1629.e5
39
Diels
S
,
Vanden Berghe
W
,
Van Hul
W
.
Insights into the multifactorial causation of obesity by integrated genetic and epigenetic analysis
.
Obes Rev
.
2020
;
21
(
7
):
e13019
40
Bandini
LG
,
Curtin
C
,
Hamad
C
, et al
.
Prevalence of overweight in children with developmental disorders in the continuous national health and nutrition examination survey (NHANES) 1999-2002
.
J Pediatr
.
2005
;
146
(
6
):
738
743
41
Ou-Yang
MC
,
Sun
Y
,
Liebowitz
M
, et al
.
Accelerated weight gain, prematurity, and the risk of childhood obesity: a meta-analysis and systematic review
.
PLoS One
.
2020
;
15
(
5
):
e0232238
42
Soch
A
,
Sominsky
L
,
De Luca
SN
,
Spencer
SJ
.
Obesity after neonatal overfeeding is independent of hypothalamic microgliosis
.
J Neuroendocrinol
.
2019
;
31
(
8
):
e12757
43
Nutrition in neurologically impaired children
.
Paediatr Child Health
.
2009
;
14
(
6
):
395
401
44
Magge
SN
,
Goodman
E
,
Armstrong
SC
;
Committee on Nutrition
;
Section on Endocrinology
;
Section on Obesity
.
The metabolic syndrome in children and adolescents: shifting the focus to cardiometabolic risk factor clustering
.
Pediatrics
.
2017
;
140
(
2
):
e20171603
45
Taveras
EM
,
Gillman
MW
,
Kleinman
KP
, et al
.
Reducing racial/ethnic disparities in childhood obesity: the role of early life risk factors
.
JAMA Pediatr
.
2013
;
167
(
8
):
731
738
46
Isong
IA
,
Rao
SR
,
Bind
MA
, et al
.
Racial and ethnic disparities in early childhood obesity
.
Pediatrics
.
2018
;
141
(
1
):
e20170865
47
Hu
K
,
Staiano
AE
.
Trends in obesity prevalence among children and adolescents aged 2 to 19 years in the US from 2011 to 2020
.
JAMA Pediatr
.
2022
;
176
(
10
):
1037
1039
48
Lange
SJ
,
Kompaniyets
L
,
Freedman
DS
, et al;
DNP3
.
Longitudinal trends in body mass index before and during the COVID-19 pandemic among persons aged 2-19 years - United States, 2018-2020
.
MMWR Morb Mortal Wkly Rep
.
2021
;
70
(
37
):
1278
1283
49
Feagin
J
,
Bennefield
Z
.
Systemic racism and U.S. health care
.
Soc Sci Med
.
2014
;
103
:
7
14
50
Aaron
DG
,
Stanford
FC
.
Is obesity a manifestation of systemic racism? A ten-point strategy for study and intervention
.
J Intern Med
.
2021
;
290
(
2
):
416
420
51
Boyd
RW
,
Lindo
EG
,
Weeks
LD
,
McLemore
MR
;
Health Affairs Blog
.
On racism: a new standard for publishing on racial health inequities
.
52
Rossen
LM
.
Neighbourhood economic deprivation explains racial/ethnic disparities in overweight and obesity among children and adolescents in the U.S.A
.
J Epidemiol Community Health
.
2014
;
68
(
2
):
123
129
53
Bandini
L
,
Danielson
M
,
Esposito
LE
, et al
.
Obesity in children with developmental and/or physical disabilities
.
Disabil Health J
.
2015
;
8
(
3
):
309
316

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