Keyword search (4,163 papers available)

"Henderson M" Authored Publications:

Title Authors PubMed ID
1 Development and Application of Children s Sex- and Age-Specific Fat-Mass and Muscle-Mass Reference Curves From Dual-Energy X-Ray Absorptiometry Data for Predicting Cardiometabolic Risk Saputra ST; Van Hulst A; Henderson M; Brugiapaglia S; Faustini C; Kakinami L; 40878792
SOH
2 Guide de pratique clinique pour la prise en charge de l obésité chez l enfant Ball GDC; Merdad R; Birken CS; Cohen TR; Goodman B; Hadjiyannakis S; Hamilton J; Henderson M; Lammey J; Morrison KM; Moore SA; Mushquash AR; Patton I; Pearce N; Ramjist JK; Lebel TR; Timmons BW; Buchholz A; Cantwell J; Cooper J; Erdstein J; Fitzpatrick-Lewis D; Hatanaka D; Lindsay P; Sajwani T; Sebastianski M; Sherifali D; Pierre JS; Ali MU; Wijesundera J; Alberga AS; Ausman C; Baluyot TC; Burke E; Dadgostar K; Delacruz B; Dettmer E; Dymarski M; Esmaeilinezhad Z; Hale I; Harnois-Leblanc S; Ho J; Gehring ND; 40721241
CONCORDIA
3 Feeling safe: a critical look at the effect of neighborhood safety features and perceptions on childhood symptoms of depression Infantino E; Barnett TA; Côté-Lussier C; Van Hulst A; Henderson M; Mathieu ME; Sabiston C; Kakinami L; 39604905
SOH
4 Adiposity and cardiac autonomic function in children with a family history of obesity Saade MB; Holden S; Kakinami L; McGrath JJ; Mathieu MÈ; Poirier P; Barnett TA; Beaucage P; Henderson M; 39304555
PERFORM
5 Children and chrono-exercise: Timing of physical activity on school and weekend days depends on sex and obesity status Reid RER; Henderson M; Barnett TA; Kakinami L; Tremblay A; Mathieu ME; 38083868
MATHSTATS
6 The longitudinal effects of maternal parenting practices on children's body mass index z-scores are lagged and differential Kakinami L; Danieles PK; Hosseininasabnajar F; Barnett TA; Henderson M; Van Hulst A; Serbin LA; Stack DM; Paradis G; 37248489
PERFORM
7 Adolescents' reports of chaos within the family home environment: Investigating associations with lifestyle behaviours and obesity Van Hulst A; Jayanetti S; Sanson-Rosas AM; Harbec MJ; Kakinami L; Barnett TA; Henderson M; 36701326
PERFORM
8 Social support and C-reactive protein in a Québec population cohort of children and adolescents Fairbank EJ; McGrath JJ; Henderson M; O' Loughlin J; Paradis G; 35731783
PSYCHOLOGY
9 Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children Kakinami L; Smyrnova A; Paradis G; Tremblay A; Henderson M; 35705336
PERFORM
10 Correction: Validation of desk‑based audits using Google Street View® to monitor the obesogenic potential of neighbourhoods in a pediatric sample: a pilot study in the QUALITY cohort Roberge JB; Contreras G; Kakinami L; Van Hulst A; Henderson M; Barnett TA; 35655311
PERFORM
11 Associations of neighborhood walkability with moderate to vigorous physical activity: an application of compositional data analysis comparing compositional and non-compositional approaches Bird M; Datta GD; Chinerman D; Kakinami L; Mathieu ME; Henderson M; Barnett TA; 35585542
MATHSTATS
12 Validation of desk-based audits using Google Street View® to monitor the obesogenic potential of neighbourhoods in a pediatric sample: a pilot study in the QUALITY cohort Roberge JB; Contreras G; Kakinami L; Van Hulst A; Henderson M; Barnett TA; 35346220
PERFORM
13 Promoting healthy lifestyle behaviours in youth: Findings from a novel intervention for children at risk of cardiovascular disease Ybarra M; Danieles PK; Barnett TA; Mathieu MÈ; Van Hulst A; Drouin O; Kakinami L; Bigras JL; Henderson M; 34992701
PERFORM
14 Body Mass Index Z Score vs Weight-for-Length Z Score in Infancy and Cardiometabolic Outcomes at Age 8-10 Years Roberge JB; Harnois-Leblanc S; McNealis V; van Hulst A; Barnett TA; Kakinami L; Paradis G; Henderson M; 34302856
PERFORM
15 Personal Social Networks and Adiposity in Adolescents: A Feasibility Study Ybarra M; Barnett TA; Yu J; Van Hulst A; Drouin O; Kakinami L; Saint-Charles J; Henderson M; 34264758
MATHSTATS
16 Determinants of attrition in a pediatric healthy lifestyle intervention: The CIRCUIT program experience Danieles PK; Ybarra M; Van Hulst A; Barnett TA; Mathieu MÈ; Kakinami L; Drouin O; Bigras JL; Henderson M; 33608233
PERFORM
17 Tune out and turn in: the influence of television viewing and sleep on lipid profiles in children. Manousaki D, Barnett TA, Mathieu ME, Maximova K, Simoneau G, Harnois-Leblanc S, Benedetti A, McGrath JJ, Henderson M, QUALITY Cohort Collaborative Group 32203106
PERFORM
18 The Associations Between Self-Perceived Actual and Ideal Body Sizes and Physical Activity Among Early Adolescents. Solomon-Krakus S, Sabiston CM, Brunet J, Castonguay AL, Henderson M 32150729
CONCORDIA
19 Neighbourhoods and obesity: A prospective study of characteristics of the built environment and their association with adiposity outcomes in children in Montreal, Canada Ghenadenik AE; Kakinami L; Van Hulst A; Henderson M; Barnett TA; 29462654
PERFORM
20 Prospective Associations Between Play Environments and Pediatric Obesity. Fitzpatrick C, Alexander S, Henderson M, Barnett TA 30354254
PERFORM
21 School food environments associated with adiposity in Canadian children. Fitzpatrick C, Datta GD, Henderson M, Gray-Donald K, Kestens Y, Barnett TA 28186100
PERFORM
22 Stigma and Its Association With Glycemic Control and Hypoglycemia in Adolescents and Young Adults With Type 1 Diabetes: Cross-Sectional Study. Brazeau AS, Nakhla M, Wright M, Henderson M, Panagiotopoulos C, Pacaud D, Kearns P, Rahme E, Da Costa D, Dasgupta K 29678801
HKAP

 

Title:Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children
Authors:Kakinami LSmyrnova AParadis GTremblay AHenderson M
Link:pubmed.ncbi.nlm.nih.gov/35705336/
DOI:10.1136/bmjopen-2021-058857
Publication:BMJ open
Keywords:community child healthepidemiologypaediatricspublic healthstatistics and research methods
PMID:35705336 Category: Date Added:2022-06-16
Dept Affiliation: PERFORM
1 PERFORM Centre, Concordia University, Montreal, Québec, Canada lisa.kakinami@concordia.ca.
2 Department of Mathematics and Statistics, Concordia University, Montreal, Québec, Canada.
3 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.
4 Département de kinésiologie, Université Laval, Quebec City, Quebec, Canada.
5 Department of Pediatrics, Université de Montréal, Montreal, Quebec, Canada.
6 Research Center of CHU Sainte Justine, Université de Montréal, Montreal, Quebec, Canada.

Description:

Objectives: Severe obesity (SO) prevalence varies between reference curve-based definitions (WHO: =99th percentile, Centers for Disease Control and Prevention (CDC): >1.2×95th percentile). Whether SO definitions differentially predict cardiometabolic disease risk is critical for proper clinical care and management but is unknown.

Design: Prospective cohort study SETTING: SO definitions were applied at baseline (2005-2008, M<sub>age</sub>=9.6 years, n=548), and outcomes (fasting lipids, glucose, homoeostatic model assessment (HOMA-IR) and blood pressure) were assessed at first follow-up (F1: 2008-2011, M<sub>age</sub>=11.6 years) and second follow-up (2015-2017, M<sub>age</sub>=16.8 years) of the Quebec Adipose and Lifestyle Investigation in Youth cohort in Montreal, Quebec.

Participants: Respondents were youth who had at least one biological parent with obesity.

Primary outcome measures: Unfavourable cardiometabolic levels of fasting blood glucose (=6.1 mmol/L), insulin resistance (HOMA-IR index =2.0), high-density lipoprotein <1.03 mmol/L, low-density lipoprotein =2.6 mmol/L and triglycerides <underline>></underline>1.24 mmol/L. Unfavourable blood pressure was defined as =90th percentile for age-adjusted, sex-adjusted and height-adjusted systolic or diastolic blood pressure.

Analysis: Area under the receiver operating characteristic curve (AUC) and McFadden psuedo R<sup>2</sup> for predicting F1 or F2 unfavourable cardiometabolic levels from baseline SO definitions were calculated. Agreement was assessed with kappas.

Results: Baseline SO prevalence differed (WHO: 18%, CDC: 6.7%). AUCs ranged from 0.52 to 0.77, with fair agreement (kappa=37%-55%). WHO-SO AUCs for detecting unfavourable HOMA-IR (AUC>0.67) and high-density lipoprotein (AUC>0.59) at F1 were statistically superior than CDC-SO (AUC>0.59 and 0.53, respectively; p<0.05). Only HOMA-IR and the presence of more than three risk factors had acceptable model fit. WHO-SO was not more predictive than WHO-obesity, but CDC-SO was statistically inferior to CDC-obesity.

Conclusion: WHO-SO is statistically superior at predicting cardiometabolic risk than CDC-SO. However, as most AUCs were generally uninformative, and obesity definitions were the same if not better than SO, the improvement may not be clinically meaningful.




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