Postmenopausal Weight Change and Incidence of Fracture
Postmenopausal Weight Change and Incidence of Fracture
We used data from the Women’s Health Initiative Observational Study and the Women’s Health Initiative Clinical Trials. At 40 clinical centers nationwide between 1993 and 1998, the Women’s Health Initiative study enrolled postmenopausal women aged 50-79 at baseline who were free from serious cardiac, pulmonary, renal, and hepatic conditions and had at least three years’ life expectancy. The three placebo controlled Women’s Health Initiative clinical trials tested several interventions among postmenopausal women: a low fat eating pattern, menopausal hormone therapy, and calcium and vitamin D supplementation. The Women’s Health Initiative Observational Study examined the predictors and natural course of important causes of morbidity and mortality in postmenopausal women. Recruitment details are available at https://www.whi.org/about/SitePages/About%20WHI.aspx. The combined studies enrolled 161 808 participants (93 676 in the observational study and 68 132 in the clinical trial). Our analytic sample consisted of 120 566 participants from the two studies for whom information was available regarding weight change (from baseline to year three) and at least one year of follow-up (after the year three visit) regarding incident fractures (Fig 1). Follow-up data were available for 31 March 1995 through 17 September 2013.
(Enlarge Image)
Figure 1.
Weight change period in relation to fracture follow-up period. Mean follow-up duration was 11 years from baseline
Participants were asked to complete baseline self assessment questionnaires. Weight and height were measured at baseline and at the third annual follow-up visit with standardized protocols. BMI was calculated as body weight in kilograms (kg) divided by the square of height in meters. Waist:hip ratio was calculated as the ratio of waist circumference (cm) to hip circumference (cm).
Our study outcome was incidence of fracture. Each year, participants were asked to report fracture events since the previous annual visit: “Has a doctor told you for the first time that you have a new broken, crushed, or fractured bone? Which bone did you break?” Questionnaire response choices included: hip, upper leg (not hip), pelvis, knee (patella), lower leg or ankle, foot (not toe), spine or back (vertebra), lower arm or wrist, hand (not finger), elbow, and upper arm or shoulder. We grouped each fracture into one of the following (mutually exclusive) categories: upper limb fracture (elbow, hand except fingers, lower arm/wrist, upper arm/humerus or shoulder), lower limb (foot except toes, knee/patella, upper leg except hip, lower leg/ankle), central body (hip, pelvis, and spine). All hip fractures were centrally adjudicated. Because our goal was to examine incidence of fracture subsequent to a change in weight between baseline and year three, we excluded fractures that were reported before the third annual follow-up visit.
The main predictor of this study was change in body weight between baseline and year three (plus/minus 90 days), operationalized in two ways. First, based on percentage change in body weight (for instance, ((weightvisit 3-weightbaseline)/weightbaseline)×100), we classified each participant’s change in body weight into one of three categories: weight loss (decrease of 5% or more since the baseline examination), stable weight (change of less than 5% from baseline weight), and weight gain (increase of 5% or more since the baseline examination). We excluded data from participants who did not undergo measurement of body weight within 90 days of the year three visit. From the two Women’s Health Initiative groups, we had information from 120 566 participants regarding weight change (from baseline to year three) and at least one year of follow-up (after the year three visit) regarding incident fractures. After exclusion of data from participants for whom we lacked data regarding covariates (10%), the sample size for analysis was 108 709.
Second, we examined unintentional and intentional weight loss as separate predictors of incident fracture. At the year three follow-up visit, participants were asked two yes/no questions: “In the past two years, did you lose five or more pounds [about ≥2.2 kg] not on purpose at any time?” (unintentional weight loss) and “In the past two years, did you lose five or more pounds on purpose at any time?” (intentional weight loss). We had responses from 81 652 participants regarding unintentional weight loss and 81 587 participants regarding intentional weight loss.
On baseline self assessment questionnaires, participants were asked whether they had experienced previous fracture (response choices were hip, spine/back/vertebra, upper arm/humerus, lower arm/wrist, hand other than finger, lower leg/ankle, foot other than toe, or other). For this study, we collapsed responses to this question into a binary (yes/no) variable.
From the baseline self report questionnaires we gathered information on age, race/ethnicity, smoking, alcohol intake (non-drinker, past drinker, <1 drink/month, <1 drink/ week, 1-<7 drinks/week, >7 drinks/week, where one drink corresponded to 12 ounces (240 ml) of beer, 6 ounces (170 ml) of wine, or 1.5 ounces (43 ml) of liquor), general health status (“in general, would you say your health is excellent, very good, good, fair, poor?”), number of falls to the ground during the past 12 months (none, once, twice, three times or more), oophorectomy or hysterectomy, recreational physical activity (total MET (metabolic equivalent) hours a week), energy expenditure from walking (MET hours a week), Rand 36-item health survey (SF-36) quality of life physical functioning score (range 0-100), comorbidity (modified Charlson index score), previous diagnosis of cancer, average protein intake from foods and beverages (g/day), dietary and supplemental calcium intake (mg/day), and dietary and supplemental vitamin D intake (IU/day).
Information regarding current use of menopausal hormone therapy, daily oral corticosteroid use, baseline use of drugs for osteoporosis (bisphosphonates, selective estrogen receptor modulators, calcitonin, parathyroid hormone, denosumab), and use of oral or injectable drugs for the treatment of diabetes (thiazolidinediones, dipeptidyl peptidase-4 inhibitors, meglitinides, glucagon-like peptide-1 agonists, insulin injection, amylin analog, sulfonylureas, biguanides, and alpha-glucosidase inhibitors) was assessed at baseline by clinic staff who examined labels of drug containers.
We used Cox proportional hazards regression to determine the association between weight change between baseline and the third annual follow-up visit and time to first fracture after the third annual follow-up visit, using individual regression models for each anatomical fracture location: central body (hip, pelvis, spine), lower extremity (upper leg except hip, knee/patella, lower leg/ankle, foot except toe), and upper extremity (upper arm/humerus, shoulder, lower arm/wrist, hand except finger, elbow). Fractures during the first three years of follow-up were excluded from this analysis. Among women who experienced fracture, duration of follow-up was defined as time to first fracture. Among women who did not experience fracture during follow-up, duration of follow-up was defined as time until last follow-up visit, or death, whichever came first. The main predictors (in separate regression models) were category of (measured) weight change (stable, weight loss, weight gain); self reported intentional weight loss of ≥2.2 kg in the two years preceding the year three follow-up visit (yes v no [reference]); and self reported unintentional weight loss (yes v no [reference]) of ≥2.2 kg in the two years before the year three follow-up visit.
Covariates included baseline height, baseline weight, age, race, smoking (current, never, past), alcohol intake, general health status, number of falls in the 12 months before baseline, oophorectomy, hysterectomy, recreational physical activity (MET hours/week), physical function score, comorbidity (Charlson index) score, baseline dietary and supplemental vitamin D intake, baseline dietary and supplemental calcium intake, use of menopausal hormone therapy at baseline, daily use of oral corticosteroids at baseline, use of oral or injectable diabetes drugs at baseline, study cohort (participation in the Women’s Health Initiative Observational Study), previous fracture, and previous diagnosis of cancer (yes/no). To explore the influence of protein intake and walking on the associations of weight loss with risk of fracture, we added dietary protein intake (g/day) and energy expenditure from walking in kcal/week/kg (MET hours a week). Continuous predictors were handled as if linear, with the exception of vitamin D and calcium intake, which were categorized (<200, 200-<400, 400-<600, and ≥600 IU/day for vitamin D and <800, 800-<1200, or ≥1200 mg/day for calcium).
We tested the assumption that the hazard ratio of the primary predictor remained constant over time by introducing a cross product term for ∆ weight*log(time) into the statistical model. The proportionality assumption was not violated.
We made the a priori decision to determine whether race/ethnicity, age category, and number of falls in the past 12 months (none, once, twice, three or more times) modified the associations between change in weight and incidence of fracture by including interaction terms in regression models. Race/ethnicity categories for interaction testing were white, black, Hispanic, Asian/Pacific Islander, and other. Age categories for interaction testing were <50-59, 60-69, and ≥70. We also examined whether associations between change in weight and fracture incidence varied by physical activity level (continuous, or binary above v below median), physical function level (continuous, or binary above v below median), and waist:hip ratio (≥0.9 v <0.9).
We also examined the results stratified by category of baseline BMI (<24.9, 25.0-29.9, >29.9) because previous studies suggest that associations between weight change and incident fracture could vary according to baseline BMI.
Because participants in the Women’s Health Initiative Dietary Modification Trial could have had weight change patterns that were different from those of the remainder of the analytic sample, we performed a sensitivity analysis in which we restricted the analytic sample to participants in that trial (samples sizes of 34 050 for lower limb fracture, 34 089 for upper limb fracture, 34 657 for central body fracture, and 34 851 for hip fracture analyses). In another sensitivity analysis, we excluded data from participants who reported the use of drugs for osteoporosis at baseline. In the final sensitivity analysis, we used the following alternative categories of weight change: stable weight (<5% change, reference), weight gain 5%-<10%, weight gain ≥10%, weight loss 5%-<10%, and weight loss ≥10%.
Statistical analyses were performed with SAS 9.3 (SAS Institute, Cary, NC).
Methods
Women's Health Initiative Study
We used data from the Women’s Health Initiative Observational Study and the Women’s Health Initiative Clinical Trials. At 40 clinical centers nationwide between 1993 and 1998, the Women’s Health Initiative study enrolled postmenopausal women aged 50-79 at baseline who were free from serious cardiac, pulmonary, renal, and hepatic conditions and had at least three years’ life expectancy. The three placebo controlled Women’s Health Initiative clinical trials tested several interventions among postmenopausal women: a low fat eating pattern, menopausal hormone therapy, and calcium and vitamin D supplementation. The Women’s Health Initiative Observational Study examined the predictors and natural course of important causes of morbidity and mortality in postmenopausal women. Recruitment details are available at https://www.whi.org/about/SitePages/About%20WHI.aspx. The combined studies enrolled 161 808 participants (93 676 in the observational study and 68 132 in the clinical trial). Our analytic sample consisted of 120 566 participants from the two studies for whom information was available regarding weight change (from baseline to year three) and at least one year of follow-up (after the year three visit) regarding incident fractures (Fig 1). Follow-up data were available for 31 March 1995 through 17 September 2013.
(Enlarge Image)
Figure 1.
Weight change period in relation to fracture follow-up period. Mean follow-up duration was 11 years from baseline
Participants were asked to complete baseline self assessment questionnaires. Weight and height were measured at baseline and at the third annual follow-up visit with standardized protocols. BMI was calculated as body weight in kilograms (kg) divided by the square of height in meters. Waist:hip ratio was calculated as the ratio of waist circumference (cm) to hip circumference (cm).
Outcomes
Our study outcome was incidence of fracture. Each year, participants were asked to report fracture events since the previous annual visit: “Has a doctor told you for the first time that you have a new broken, crushed, or fractured bone? Which bone did you break?” Questionnaire response choices included: hip, upper leg (not hip), pelvis, knee (patella), lower leg or ankle, foot (not toe), spine or back (vertebra), lower arm or wrist, hand (not finger), elbow, and upper arm or shoulder. We grouped each fracture into one of the following (mutually exclusive) categories: upper limb fracture (elbow, hand except fingers, lower arm/wrist, upper arm/humerus or shoulder), lower limb (foot except toes, knee/patella, upper leg except hip, lower leg/ankle), central body (hip, pelvis, and spine). All hip fractures were centrally adjudicated. Because our goal was to examine incidence of fracture subsequent to a change in weight between baseline and year three, we excluded fractures that were reported before the third annual follow-up visit.
Predictor Variables
The main predictor of this study was change in body weight between baseline and year three (plus/minus 90 days), operationalized in two ways. First, based on percentage change in body weight (for instance, ((weightvisit 3-weightbaseline)/weightbaseline)×100), we classified each participant’s change in body weight into one of three categories: weight loss (decrease of 5% or more since the baseline examination), stable weight (change of less than 5% from baseline weight), and weight gain (increase of 5% or more since the baseline examination). We excluded data from participants who did not undergo measurement of body weight within 90 days of the year three visit. From the two Women’s Health Initiative groups, we had information from 120 566 participants regarding weight change (from baseline to year three) and at least one year of follow-up (after the year three visit) regarding incident fractures. After exclusion of data from participants for whom we lacked data regarding covariates (10%), the sample size for analysis was 108 709.
Second, we examined unintentional and intentional weight loss as separate predictors of incident fracture. At the year three follow-up visit, participants were asked two yes/no questions: “In the past two years, did you lose five or more pounds [about ≥2.2 kg] not on purpose at any time?” (unintentional weight loss) and “In the past two years, did you lose five or more pounds on purpose at any time?” (intentional weight loss). We had responses from 81 652 participants regarding unintentional weight loss and 81 587 participants regarding intentional weight loss.
Other Variables
On baseline self assessment questionnaires, participants were asked whether they had experienced previous fracture (response choices were hip, spine/back/vertebra, upper arm/humerus, lower arm/wrist, hand other than finger, lower leg/ankle, foot other than toe, or other). For this study, we collapsed responses to this question into a binary (yes/no) variable.
From the baseline self report questionnaires we gathered information on age, race/ethnicity, smoking, alcohol intake (non-drinker, past drinker, <1 drink/month, <1 drink/ week, 1-<7 drinks/week, >7 drinks/week, where one drink corresponded to 12 ounces (240 ml) of beer, 6 ounces (170 ml) of wine, or 1.5 ounces (43 ml) of liquor), general health status (“in general, would you say your health is excellent, very good, good, fair, poor?”), number of falls to the ground during the past 12 months (none, once, twice, three times or more), oophorectomy or hysterectomy, recreational physical activity (total MET (metabolic equivalent) hours a week), energy expenditure from walking (MET hours a week), Rand 36-item health survey (SF-36) quality of life physical functioning score (range 0-100), comorbidity (modified Charlson index score), previous diagnosis of cancer, average protein intake from foods and beverages (g/day), dietary and supplemental calcium intake (mg/day), and dietary and supplemental vitamin D intake (IU/day).
Information regarding current use of menopausal hormone therapy, daily oral corticosteroid use, baseline use of drugs for osteoporosis (bisphosphonates, selective estrogen receptor modulators, calcitonin, parathyroid hormone, denosumab), and use of oral or injectable drugs for the treatment of diabetes (thiazolidinediones, dipeptidyl peptidase-4 inhibitors, meglitinides, glucagon-like peptide-1 agonists, insulin injection, amylin analog, sulfonylureas, biguanides, and alpha-glucosidase inhibitors) was assessed at baseline by clinic staff who examined labels of drug containers.
Statistical Analysis
We used Cox proportional hazards regression to determine the association between weight change between baseline and the third annual follow-up visit and time to first fracture after the third annual follow-up visit, using individual regression models for each anatomical fracture location: central body (hip, pelvis, spine), lower extremity (upper leg except hip, knee/patella, lower leg/ankle, foot except toe), and upper extremity (upper arm/humerus, shoulder, lower arm/wrist, hand except finger, elbow). Fractures during the first three years of follow-up were excluded from this analysis. Among women who experienced fracture, duration of follow-up was defined as time to first fracture. Among women who did not experience fracture during follow-up, duration of follow-up was defined as time until last follow-up visit, or death, whichever came first. The main predictors (in separate regression models) were category of (measured) weight change (stable, weight loss, weight gain); self reported intentional weight loss of ≥2.2 kg in the two years preceding the year three follow-up visit (yes v no [reference]); and self reported unintentional weight loss (yes v no [reference]) of ≥2.2 kg in the two years before the year three follow-up visit.
Covariates included baseline height, baseline weight, age, race, smoking (current, never, past), alcohol intake, general health status, number of falls in the 12 months before baseline, oophorectomy, hysterectomy, recreational physical activity (MET hours/week), physical function score, comorbidity (Charlson index) score, baseline dietary and supplemental vitamin D intake, baseline dietary and supplemental calcium intake, use of menopausal hormone therapy at baseline, daily use of oral corticosteroids at baseline, use of oral or injectable diabetes drugs at baseline, study cohort (participation in the Women’s Health Initiative Observational Study), previous fracture, and previous diagnosis of cancer (yes/no). To explore the influence of protein intake and walking on the associations of weight loss with risk of fracture, we added dietary protein intake (g/day) and energy expenditure from walking in kcal/week/kg (MET hours a week). Continuous predictors were handled as if linear, with the exception of vitamin D and calcium intake, which were categorized (<200, 200-<400, 400-<600, and ≥600 IU/day for vitamin D and <800, 800-<1200, or ≥1200 mg/day for calcium).
We tested the assumption that the hazard ratio of the primary predictor remained constant over time by introducing a cross product term for ∆ weight*log(time) into the statistical model. The proportionality assumption was not violated.
We made the a priori decision to determine whether race/ethnicity, age category, and number of falls in the past 12 months (none, once, twice, three or more times) modified the associations between change in weight and incidence of fracture by including interaction terms in regression models. Race/ethnicity categories for interaction testing were white, black, Hispanic, Asian/Pacific Islander, and other. Age categories for interaction testing were <50-59, 60-69, and ≥70. We also examined whether associations between change in weight and fracture incidence varied by physical activity level (continuous, or binary above v below median), physical function level (continuous, or binary above v below median), and waist:hip ratio (≥0.9 v <0.9).
We also examined the results stratified by category of baseline BMI (<24.9, 25.0-29.9, >29.9) because previous studies suggest that associations between weight change and incident fracture could vary according to baseline BMI.
Because participants in the Women’s Health Initiative Dietary Modification Trial could have had weight change patterns that were different from those of the remainder of the analytic sample, we performed a sensitivity analysis in which we restricted the analytic sample to participants in that trial (samples sizes of 34 050 for lower limb fracture, 34 089 for upper limb fracture, 34 657 for central body fracture, and 34 851 for hip fracture analyses). In another sensitivity analysis, we excluded data from participants who reported the use of drugs for osteoporosis at baseline. In the final sensitivity analysis, we used the following alternative categories of weight change: stable weight (<5% change, reference), weight gain 5%-<10%, weight gain ≥10%, weight loss 5%-<10%, and weight loss ≥10%.
Statistical analyses were performed with SAS 9.3 (SAS Institute, Cary, NC).
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