Identifying Persons in Need of Weight-loss Treatment: Evaluation of Potential Treatment Algorithms Caitlin Mason School of Physical and Health Education Queen’s University, Kingston, ON

 Obesity is an increasingly significant Canadian health issue: 59.2% of adults are overweight or obese (CCHS, 2004) 1 in 10 premature deaths can be attributed to overweight and obesity (Katzmarzyk & Ardern, 2004) associated with substantial health care costs (~$4.3 billion annually) (Katzmarzyk & Janssen, 2004) Background

 Screening for overweight and obesity is now considered an integral part of routine medical assessments (Douketis et al., 2005) Canadian Guidelines for Body Weight Classification in Adults  In 2003, Health Canada published the Canadian Guidelines for Body Weight Classification in Adults  Canadian guidelines do not include recommendations for weight loss interventions/treatment BMIWCCVD risk factors  In 1998, the U.S. NIH Obesity Education Initiative Expert Panel proposed a treatment algorithm that uses measures of BMI (kg/m 2 ), WC (cm), and CVD risk factors to identify patients for weight loss treatment.

Measure height, weight and waist circumference Calculate BMI BMI  25 or Waist >88 cm (F) >102 cm (M) Assess Risk Factors BMI  30 OR [BMI or waist >88 cm (F) >102 cm (M) AND  2 risk factors] Treatment Options Lifestyle therapy Pharmacotherapy Weight loss surgery RISK FACTORS Smoking Hypertension High LDL-C Low HDL-C Previous coronary event Diabetes  45y (M)  55 y (F) Does patient want to lose weight? Educate/ Reinforce No Yes Patient Encounter No

 Quantify the proportion of Canadians meeting the criteria of the existing NIH algorithm and derivative algorithms for weight loss treatment Purpose Study 1: C Mason & PT Katzmarzyk. Application of obesity treatment algorithms to the Canadian population. Euro J Clin Nutr 2005;59:

The following four treatment algorithms were compared: A.NIH algorithm: BMI>30 OR {[BMI OR WC >88cm(F), >102cm(M)] AND  2 CVD risk factors} B.BMI alone: BMI>30 OR {BMI AND  2 CVD risk factors} C. WC alone (single cutoff): WC >88cm (F) >102cm(M) AND  2 CVD risk factors D.WC alone (tiered cutoffs): WC >88cm(F), >102cm(M) OR {WC >80cm(F), >94cm(M)] AND  2 CVD risk factors}

 Canadian Heart Health Surveys (CHHS) Waist circumference measures made in only five central provinces, thus analysis limited to: Alberta, Saskatchewan, Manitoba, Ontario, and Quebec Methods

Participants were assigned to a high CVD risk status based on presence of 2 or more CVD risk factors, including: Current smoking Hypertension ( SBP  140 mmHg, DBP  90 mmHg ) High LDL-C (>4.13 mmol/L ) Low HDL-C (<0.90 mmol/L ) Self-report diabetes Age (M:  45 yrs, F:  55 yrs ) Previous cardiovascular event  2 CVD RISK FACTORS  Family history was excluded (not collected in all provinces). Subset analysis confirmed this did not make a difference (23.6% vs. 23.9%) in the proportion of subjects recommended treatment.

ALGORITHM Overall (n=7501) Men (n=3678) Women (n=3823) NIH algorithm: BMI  30 OR {[BMI OR WC >88cm(F), >102cm(M)] AND  2 CVD risk factors} BMI alone algorithm: BMI  30 OR {BMI AND  2 CVD risk factors} WC algorithm (single cutoff): WC >88cm (F) >102cm(M) AND  2 CVD risk factors WC algorithm (tiered cutoffs): WC >88cm(F), >102cm(M) OR {[WC >80cm(F), >94cm(M)] AND  2 CVD risk factors} Proportion of CHHS sample meeting the criteria for each of 4 algorithms for weight-loss treatment

NIH algorithm BMI alone algorithm WC algorithm (single cut-off) WC algorithm (tiered cut-off) 6.7% 18.2% 5.8% 0.01% 3.8% Proportion of CHHS sample meeting the criteria for each of 4 algorithms for weight-loss treatment

 Approximately 1 in 4 Canadians would be eligible for weight loss treatment  Within the context of the NIH algorithm, the utility of WC appears constrained by the thresholds used Implications

Study 2: Mason C, PT Katzmarzyk & SN Blair. Eligibility for obesity treatment and risk of mortality in men. Obes Res 2005; In press.  Evaluate the utility of the NIH algorithm in identifying men at increased risk of premature mortality  Examine the effects of physical fitness on the risk of mortality associated with each outcome of the algorithm Purpose

Subjects 18,666 men from the Aerobics Center Longitudinal Study (ACLS), Dallas, TX aged years (mean = 42.9  9.2 yrs) clinical evaluations between 1979 and 1995 Methods

Measures Physical exam: Height Weight Waist circumference Resting blood pressure Fasting blood sample Personal & family medical history Health habits questionnaire Cardiorespiratory fitness: maximal treadmill exercise test categorized as fit or unfit based on age-specific maximal METs lowest quintile (20%) classified as unfit, upper four quintiles(80%) as fit

Participants were assigned to a high CVD risk status based on presence of 2 or more CVD risk factors, including: Current smoking Hypertension ( SBP  140 mmHg, DBP  90 mmHg ) High LDL-C (>4.13 mmol/L ) Low HDL-C (<0.90 mmol/L ) Elevated fasting blood glucose (  6.1 mmol/L ) Diabetes Age (  45 yrs ) Previous cardiovascular event Family history of premature cardiovascular disease  2 CVD RISK FACTORS

Mortality Surveillance Linked with the NCHS National Death Index Coded according to ICD-9 codes CVD mortality defined as codes 390 to Until death or December 31 st, 1996 Analysis Cox proportional hazards regression Adjusted for age and year of examination

Relative risks of CVD mortality by NIH risk classification CVD Mortality Hazard Ratio Normal Weight Overweight <2 CVD risk factors Overweight  2 CVD risk factors Obese <2 CVD risk factors Obese  2 CVD risk factors 1.0 [reference] 0.72 [ ] 1.67 [ ] 1.69 [ ] 3.31 [ ] Maintain weight Weight loss treatment 1.0 [reference] 2.16 [ ] 1.37 [ ] 1.0 [reference] 0.60 [ ] 1.14 [ ] 0.83 [ ] 1.50 [ ] CRF adjusted

Relative risk of all-cause and CVD mortality according to NIH criteria for weight loss, by fitness Maintain weight Weight loss Treatment Maintain weight Weight loss Treatment * p < 0.05 from fit men who would be recommended to maintain current body weight * * * * All-Cause CVD R R

Conclusions The NIH obesity treatment algorithm is useful in identifying men at increased risk of premature mortality Risk of obesity-related mortality is significantly attenuated by cardiorespiratory fitness Including an assessment of fitness would help improve risk stratification among all groups of patients

Taken together… Provide support for the NIH algorithm as a valuable clinical tool Supports the promotion of physical activity as a valuable prevention strategy Further research needed to discern the most appropriate anthropometric cut-offs

The Canadian Heart Health Surveys Follow-up Study is a New Emerging Team, funded by the Canadian Institutes for Health Research and the Heart and Stroke Foundation of Canada