Usage Information
Longitudinal associations of insulin resistance with change in bone mineral density in midlife women
Abstract
BACKGROUND The effects of insulin resistance on bone mineral density (BMD) are unclear.METHODS In Study of Women’s Health Across the Nation (SWAN) participants, we used multivariable regression to test average insulin resistance (homeostatic model assessment of insulin resistance, HOMA-IR) and rate of change in insulin resistance as predictors of rate of change in lumbar spine (LS) and femoral neck (FN) BMD in 3 stages: premenopause (n = 861), menopause transition (MT) (n = 571), and postmenopause (n = 693). Models controlled for age, average BW, change in BW, cigarette use, race and ethnicity, and study site.RESULTS The relation between HOMA-IR and BMD decline was biphasic. When average log2HOMA-IR was less than 1.5, greater HOMA-IR was associated with slower BMD decline; i.e., each doubling of average HOMA-IR in premenopause was associated with a 0.0032 (P = 0.01, LS) and 0.0041 (P = 0.004, FN) g/cm2 per year slower BMD loss. When greater than or equal to 1.5, average log2HOMA-IR was not associated with BMD change. In women in whom HOMA-IR decreased in premenopause, the association between the HOMA-IR change rate and BMD change rate was positive; i.e, slower HOMA-IR decline was associated with slower BMD loss. In women in whom insulin resistance increased in premenopause, the association was negative; i.e, faster HOMA-IR rise was associated with faster BMD decline. Associations of average HOMA-IR and HOMA-IR change rate with BMD change rate were similar in postmenopause, but weaker during the MT.CONCLUSION When it decreases, insulin resistance is associated with BMD preservation; when it increases, insulin resistance is associated with BMD loss.FUNDING The SWAN has grant support from the NIH of the Department of Health and Human Services (DHHS) through the NIH National Institute on Aging (NIA), National Institute of Nursing Research (NINR), and Office of Research on Women’s Health (ORWH) (grants U01NR004061, U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495, and U19AG063720).
Authors
Albert Shieh, Gail A. Greendale, Jane A. Cauley, Preethi Srikanthan, Arun S. Karlamangla
Usage data is cumulative from February 2025 through February 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 997 | 143 |
| 140 | 26 | |
| Figure | 128 | 0 |
| Table | 156 | 0 |
| Supplemental data | 46 | 1 |
| Citation downloads | 108 | 0 |
| Totals | 1,575 | 170 |
| Total Views | 1,745 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
