Vasomotor Symptom Trajectories and Risk of Incident Diabetes

Monique M Hedderson; Emily F Liu; Catherine Lee; Samar R El Khoudary; Ellen B Gold; Carol A Derby; Rebecca C Thurston

doi:10.1001/jamanetworkopen.2024.43546

. 2024 Oct 31;7(10):e2443546. doi: 10.1001/jamanetworkopen.2024.43546

Vasomotor Symptom Trajectories and Risk of Incident Diabetes

Monique M Hedderson ^1,^✉, Emily F Liu ¹, Catherine Lee ¹, Samar R El Khoudary ², Ellen B Gold ³, Carol A Derby ⁵, Rebecca C Thurston ^1,⁴

¹Division of Research, Kaiser Permanente Northern California, Pleasanton

²Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania

³Department of Public Health Sciences, School of Medicine, University of California, Davis

⁴Department of Psychiatry, University of Pittsburgh, Pennsylvania

⁵Departments of Neurology and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York

Accepted for Publication: September 15, 2024.

Published: October 31, 2024. doi:10.1001/jamanetworkopen.2024.43546

^✉

Corresponding Author: Monique M. Hedderson, PhD, Division of Research, Kaiser Permanente, 4480 Hacienda Dr, Bldg B, Pleasanton, CA 94588 (monique.m.hedderson@kp.org).

Author Contributions: Dr Hedderson had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hedderson, Lee, El Khoudary, Gold, Thurston.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Hedderson, Liu, El Khoudary, Gold.

Critical review of the manuscript for important intellectual content: Hedderson, Lee, El Khoudary, Gold, Derby, Thurston.

Statistical analysis: Hedderson, Liu, Lee.

Obtained funding: Hedderson, Gold, Derby, Thurston.

Administrative, technical, or material support: Hedderson, El Khoudary, Gold, Derby.

Supervision: Hedderson, Thurston.

Conflict of Interest Disclosures: Dr Gold reported receiving grants from the National Institutes of Health (NIH) outside the submitted work. Dr Thurston reported receiving personal fees from Astellas Pharma, Bayer, Hello Therapeutics, Happify Health, and Vira Health outside the submitted work. No other disclosures were reported.

Funding/Support: The Study of Women’s Health Across the Nation (SWAN) has grant support from the NIH, Department of Health and Human Services, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR) and the NIH Office of Research on Women’s Health (ORWH) (grants Nos. U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495, and U19AG063720).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH, or the NIH.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank the study staff at each site and all the women who participated in SWAN. Principal investigators at the clinical centers: Carrie Karvonen-Gutierrez, PhD, 2021 to present; Siobán Harlow, PhD, 2011 to 2021; MaryFran Sowers, PhD, 1994-2011 (University of Michigan, Ann Arbor); Sherri-Ann Burnett-Bowie, MD, MPH, 2020 to present; Joel Finkelstein, MD, 1999 to 2020; Robert Neer, MD, 1994 to 1999 (Massachusetts General Hospital, Boston); Imke Janssen, PhD, 2020 to present; Howard Kravitz, DO, MPH, 2009 to 2020; Lynda Powell, PhD, 1994 to 2009 (Rush University Medical Center, Chicago, Illinois); Elaine Waetjen, MD, and Monique Hedderson, PhD, 2020 to present; Ellen Gold, PhD, 1994 to 2020 (University of California, Davis/Kaiser); Arun Karlamangla, MD, 2020 to present; Gail Greendale, MD, 1994 to 2020 (University of California, Los Angeles); Carol Derby, PhD, 2011 to present; Rachel Wildman PhD, MPH, 2010 to 2011; Nanette Santoro, MD, 2004 to 2010 (Albert Einstein College of Medicine, Bronx, New York); Gerson Weiss, MD, 1994 to 2004 (University of Medicine and Dentistry, New Jersey Medical School, Newark); Rebecca Thurston, PhD, 2020 to present; and Karen Matthews, PhD, 1994 to 2020 (University of Pittsburgh, Pennsylvania). At the NIH Program Office: Rosaly Correa-de-Araujo, MD, PhD, 2020 to present; Chhanda Dutta, PhD, 2016 to present; Winifred Rossi, MA, 2012 to 2016; Sherry Sherman, PhD, 1994 to 2012; Marcia Ory, PhD, 1994 to 2001 (NIA and NINR, Bethesda, Maryland: Program Officers). At the central laboratory: Daniel McConnell, PhD (University of Michigan, Ann Arbor, Central Ligand Assay Satellite Services). At the coordinating center: Maria Mori Brooks, PhD, 2012 to present; Kim Sutton-Tyrrell, PhD, 2001 to 2012 (University of Pittsburgh, Pennsylvania); Sonja McKinlay, PhD, 1995 to 2001 ( New England Research Institutes, Watertown, Massuchusetts). Steering Committee: Susan Johnson, MD (University of Iowa, Iowa City), Current Chair; Chris Gallagher, MD (Creighton University, Omaha, Nebraska), Former Chair.

^✉

Corresponding author.

PMCID: PMC11528338 PMID: 39480425

Abstract

This cohort study evaluates the association between frequency and trajectories of vasomotor symptoms with incident type 2 diabetes among premenopausal or perimenopausal women in the US.

Introduction

Vasomotor symptoms (VMS; night sweats and hot flashes) are the hallmark signs of menopause. Growing evidence suggests an association between VMS and the increased cardiometabolic risk experienced in women during and after the menopausal transition (MT).¹ While most women experience VMS,² the frequency and the temporal patterns vary dramatically across subgroups of women.³ Past studies examined the association of VMS at 1 time point with diabetes; thus, it remains unknown whether VMS trajectories across the MT are associated with risk of type 2 diabetes (T2D). We examined the associations of frequency and trajectories of VMS with incident T2D over the MT in the Study of Women’s Health Across the Nation (SWAN).

Methods

SWAN is a prospective cohort of US women who were premenopausal or early perimenopausal at baseline and assessed at up to 13 approximately annual follow-up visits at 7 clinical sites (eTable).⁴ All protocols were approved by the sites’ institutional review boards, and all study participants provided informed written consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. The analytic sample excluded participants who had diabetes or used exogenous hormones at baseline, did not have any follow-up visits or at least 3 visits before diabetes diagnosis, or were missing VMS information.

At each follow-up visit, participants reported how often they experienced hot flashes and/or night sweats in the past 2 weeks. Women were defined as having diabetes at each visit if they reported use of antidiabetic medication, had 2 consecutive visits with fasting glucose 126 mg/dL (to convert to mmol/L, multiply by .0555) or more while not receiving steroids, or had 2 visits with self-reported diabetes and 1 visit with fasting glucose 126 mg/dL or more (eMethods in Supplement 1).

We tested associations between VMS and VMS trajectories and incident diabetes using discrete-time hazard models⁵ via fitted generalized linear models with a complementary log-log link to generate hazard ratios (HRs) and 95% CIs. VMS were also modeled using a group-based trajectory approach⁶ to identify VMS patterns during follow-up. All models were adjusted for time (study visit) and site; minimally adjusted models included self-reported race and ethnicity (Black, Chinese, Hispanic, Japanese, and White; assessed because of concerns regarding differences in health outcomes across these groups), baseline age and educational attainment, and time-varying body mass index (BMI), physical activity, smoking status, alcohol consumption, and MT stage; fully adjusted models additionally included sex hormones (eMethods in Supplement 1). Analyses were conducted in RStudio version 2023.09.1 (R Project for Statistical Computing). All results were considered statistically significant at a 2-sided P ≤ .05. Data were analyzed from January 2021 to June 2024.

Results

A total of 2761 SWAN participants at baseline were midlife and racially and ethnically diverse (mean [SD] age, 46 [3] years; 737 [27%] Black, 265 [9.6%] Japanese, and 1345 [49%] White) (Table 1). At baseline, 764 (28%) reported VMS 1 to 5 days per 2-week period, 280 (10%) reported VMS 6 or more days per week, and 1716 (62%) reported no VMS; 338 (12.2%) developed diabetes during follow-up (Table 1).

Table 1. Baseline Characteristics of Study Sample From Study of Women’s Health Across the Nation Population.

Characteristic	Patients, No. (%) (N = 2761)
Age, mean (SD), y	46 (3)
Vasomotor symptom frequency, any
None	1716 (62)
1-5 d per 2 weeks	764 (28)
≥6 d per 2 weeks	280 (10)
VMS trajectory class, any
Consistently low	704 (26)
Consistently high	846 (31)
Early onset	694 (25)
Late onset	512 (19)
Unknown	5 (0.2)
Race and ethnicity
Black	737 (27)
Chinese	234 (8.5)
Hispanic	180 (6.5)
Japanese	265 (9.6)
White	1345 (49)
Education
High school or less	601 (22)
Some college	871 (32)
≥4-y College	1265 (46)
Annual household income, US $
Less than 10 000	139 (5.2)
10 000-19 999	195 (7.3)
20 000-34 999	412 (15)
35 000-49 999	490 (18)
50 000-74 999	639 (24)
75 000-99 999	376 (14)
100 000-149 999	310 (12)
≥150 000	126 (4.7)
Menopausal transition stage
Premenopause	1524 (55)
Early perimenopause	1237 (45)
Body mass index, mean (SD)^a	28 (7)
Smoking status
Never smoked	1611 (58)
Past only	708 (26)
Current smoker	442 (16)
Alcohol use
None	1323 (48)
<1 drink per week	302 (11)
1-7 drinks per week	728 (26)
>7 drinks per week	405 (15)
Physical activity score, mean (SD)^b	7.73 (1.77)
Sex hormone-binding globulin, median (IQR), µg/mL	3.99 (2.76-5.51)
Testosterone, median (IQR), ng/dL	41 (30-56)
Free Androgen Index, median (IQR)	3.4 (2.1-5.8)

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SI conversion factor: To convert testosterone to nmol/L, multiply by 0.0347; sex hormone-binding globulin to nmol/L, multiply by 8.896.

^{^a}

Calculated as weight in kilograms divided by height in meters squared.

^{^b}

Physical activity score is the sum of 3 indices of sports and exercise, active living, and household/caregiving domains of physical activity. The values of the score range from 3 to 15.

More frequent time-varying VMS were associated with increased risk of incident diabetes (relative to no VMS: frequent VMS HR, 1.45; 95% CI, 1.11-1.95; infrequent VMS HR, 1.30; 95% CI, 1.00-1.70), adjusted for covariates (Table 2). Four VMS trajectories were identified, with some patient falling into a fifth, unknown category: (1) consistently low probability of VMS (704 patients [26%]), (2) persistently high probability of VMS (846 patients [31%]), (3) early onset-initial high probability of VMS that decreased over time (694 patients [25%]), (4) late onset-initial low probability of VMS that increased over time (512 patients [19%]), and unknown (5 patients [0.2%]). Women with persistently high VMS had an increased risk of diabetes compared with consistently low VMS (HR, 1.50; 95% CI, 1.12-2.02). Results were similar for both hot flashes and night sweats (Table 2).

Table 2. Crude and Adjusted Hazard Ratio (HR) of Incident Diabetes by Frequency of Any Vasomotor Symptoms (VMS) at Baseline and Time-Varying and by VMS Trajectories^a^,^b.

Frequency or trajectory of symptoms	HR (95% CI)
Frequency or trajectory of symptoms	Unadjusted	Adjusted model 1^b	Adjusted model 2^c
Frequency of any VMS
Baseline: None	1 [Reference]	1 [Reference]	1 [Reference]
Baseline: 1-5 d per 2 weeks	1.17 (0.91-1.49)	1.07 (0.83-1.38)	1.03 (0.80-1.33)
Baseline: ≥6 d per 2 weeks	1.71 (1.26-2.35)	1.25 (0.91-1.73)	1.21 (0.88-1.67)
Time-varying: none	1 [Reference]	1 [Reference]	1 [Reference]
Time-varying: 1-5 d per 2 weeks	1.20 (0.93-1.56)	1.30 (1.00-1.70)	1.26 (0.97-1.63)
Time-varying: ≥6 d per 2 weeks	1.34 (1.02-1.77)	1.45 (1.11-1.95)	1.41 (1.05-1.87)
Frequency of hot flashes
Baseline: none	1 [Reference]	1 [Reference]	1 [Reference]
Baseline: 1-5 d per 2 weeks	1.54 (1.20-1.98)	1.38 (1.06-1.78)	1.34 (1.04-1.73)
Baseline: ≥6 d per 2 weeks	1.61 (1.13-2.29)	1.20 (0.83-1.74)	1.20 (0.83-1.73)
Time-varying: none	1 [Reference]	1 [Reference]	1 [Reference]
Time-varying: 1-5 d per 2 weeks	1.17 (0.91-1.52)	1.27 (0.98-1.65)	1.23 (0.95-1.60)
Time-varying: ≥6 d per 2 weeks	1.27 (0.96-1.68)	1.35 (1.01-1.79)	1.30 (0.97-1.73)
Frequency of night sweats
Baseline: none	1 [Reference]	1 [Reference]	1 [Reference]
Baseline: 1-5 d per 2 weeks	1.03 (0.79-1.34)	0.90 (0.68-1.18)	0.90 (0.68-1.18)
Baseline: ≥6 d per 2 weeks	1.56 (1.07-2.28)	1.14 (0.77-1.68)	1.09 (0.74-1.62)
Time-varying: none	1 [Reference]	1 [Reference]	1 [Reference]
Time-varying: 1-5 d per 2 weeks	1.09 (0.84-1.42)	1.18 (0.90-1.55)	1.14 (0.87-1.49)
Time-varying: ≥6 d per 2 weeks	1.44 (1.06-1.95)	1.55 (1.13-2.12)	1.46 (1.07-2.00)
VMS trajectories
Any VMS
Persistently high (n = 846)	1.47 (1.11-1.95)	1.50 (1.12-2.02)	1.42 (1.06-1.92)
Early onset (n = 694)	0.87 (0.62-1.20)	0.83 (0.59-1.16)	0.77 (0.56-1.08)
Late onset (n = 512)	0.76 (0.52-1.10)	0.98 (0.67-1.44)	0.97 (0.66-1.43)
Consistently low (n = 704)	1 [Reference]	1 [Reference]	1 [Reference]
Hot flashes
Persistently high (n = 707)	1.51 (1.16-1.96)	1.64 (1.25-2.17)	1.56 (1.18-2.05)
Early onset (n = 658)	0.85 (0.62-1.15)	1.27 (0.92-1.75)	1.28 (0.93-1.76)
Late onset (n = 532)	1.03 (0.75-1.41)	1.02 (0.74-1.42)	0.94 (0.67-1.30)
Consistently low (n = 864)	1 [Reference]	1 [Reference]	1 [Reference]
Night sweats
Persistently high (n = 514)	1.93 (1.36-2.73)	1.86 (1.30-2.67)	1.73 (1.21-2.48)
Early onset (n = 521)	1.49 (1.03-2.17)	1.33 (0.91-1.95)	1.22 (0.83-1.79)
Late onset (n = 1157)	1.43 (1.02-2.00)	1.21 (0.86-1.72)	1.23 (0.87-1.74)
Consistently low (n = 564)	1 [Reference]	1 [Reference]	1 [Reference]

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^{^a}

Crude/unadjusted model includes site and site visit number as covariates.

^{^b}

Model adjusted for site, visit number, race and ethnicity, baseline age and education, time-varying body mass index, physical activity score, smoking status, alcohol consumption, and menopause transition stage.

^{^c}

Model further adjusted for sex hormone binding globulin and testosterone levels at baseline.

Discussion

The study was limited to women with at least 3 visits before diabetes diagnosis to quantify trajectories; therefore, women who have a short time from VMS onset to diabetes are not represented. Women who had different patterns of VMS in the past 2 weeks from the other parts of the year may have been misclassified. SWAN did not measure hemoglobin A_1C, which is typically used to diagnose diabetes.

In this prospective cohort study, frequent VMS or a trajectory of persistent VMS were associated with a 50% increased risk of diabetes. Women with frequent and/or persistent VMS over the MT may represent a high-risk group to target for diabetes prevention.

Supplement 1.

eMethods. Diabetes Ascertainment

eTable. Number of SWAN Women Contributing to the Analysis at Each SWAN Visit

eReferences

jamanetwopen-e2443546-s001.pdf^{(251.1KB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2443546-s002.pdf^{(14.1KB, pdf)}

References

1.Gray KE, Katon JG, LeBlanc ES, et al. Vasomotor symptom characteristics: are they risk factors for incident diabetes? Menopause. 2018;25(5):520-530. doi: 10.1097/GME.0000000000001033 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Gold EB, Colvin A, Avis N, et al. Longitudinal analysis of the association between vasomotor symptoms and race/ethnicity across the menopausal transition: study of women’s health across the nation. Am J Public Health. 2006;96(7):1226-1235. doi: 10.2105/AJPH.2005.066936 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Tepper PG, Brooks MM, Randolph JF Jr, et al. Characterizing the trajectories of vasomotor symptoms across the menopausal transition. Menopause. 2016;23(10):1067-1074. doi: 10.1097/GME.0000000000000676 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Sowers M, Crawford SL, Sternfeld B, et al. Chapter 11 - SWAN: A Multicenter, Multiethnic, Community-Based Cohort Study of Women and the Menopausal Transition. In: Lobo RA, Kelsey J, Marcus R, eds. Menopause. Academic Press; 2000:175-188. doi: 10.1016/B978-012453790-3/50012-3 [DOI] [Google Scholar]
5.Allison PD. Discrete-time methods for the analysis of event histories. Sociol Methodol. 1982;13:61-98. doi: 10.2307/270718 [DOI] [Google Scholar]
6.Jones BL, Nagin DS, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res. 2011;29(3):374-393. doi: 10.1177/0049124101029003005 [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eMethods. Diabetes Ascertainment

eTable. Number of SWAN Women Contributing to the Analysis at Each SWAN Visit

eReferences

jamanetwopen-e2443546-s001.pdf^{(251.1KB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2443546-s002.pdf^{(14.1KB, pdf)}

[zld240210r1] 1.Gray KE, Katon JG, LeBlanc ES, et al. Vasomotor symptom characteristics: are they risk factors for incident diabetes? Menopause. 2018;25(5):520-530. doi: 10.1097/GME.0000000000001033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zld240210r2] 2.Gold EB, Colvin A, Avis N, et al. Longitudinal analysis of the association between vasomotor symptoms and race/ethnicity across the menopausal transition: study of women’s health across the nation. Am J Public Health. 2006;96(7):1226-1235. doi: 10.2105/AJPH.2005.066936 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zld240210r3] 3.Tepper PG, Brooks MM, Randolph JF Jr, et al. Characterizing the trajectories of vasomotor symptoms across the menopausal transition. Menopause. 2016;23(10):1067-1074. doi: 10.1097/GME.0000000000000676 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zld240210r4] 4.Sowers M, Crawford SL, Sternfeld B, et al. Chapter 11 - SWAN: A Multicenter, Multiethnic, Community-Based Cohort Study of Women and the Menopausal Transition. In: Lobo RA, Kelsey J, Marcus R, eds. Menopause. Academic Press; 2000:175-188. doi: 10.1016/B978-012453790-3/50012-3 [DOI] [Google Scholar]

[zld240210r5] 5.Allison PD. Discrete-time methods for the analysis of event histories. Sociol Methodol. 1982;13:61-98. doi: 10.2307/270718 [DOI] [Google Scholar]

[zld240210r6] 6.Jones BL, Nagin DS, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res. 2011;29(3):374-393. doi: 10.1177/0049124101029003005 [DOI] [Google Scholar]

PERMALINK

Vasomotor Symptom Trajectories and Risk of Incident Diabetes

Monique M Hedderson, PhD

Emily F Liu, MPH

Catherine Lee, PhD

Samar R El Khoudary, PhD

Ellen B Gold, PhD

Carol A Derby, PhD

Rebecca C Thurston, PhD

Abstract

Introduction

Methods

Results

Table 1. Baseline Characteristics of Study Sample From Study of Women’s Health Across the Nation Population.

Table 2. Crude and Adjusted Hazard Ratio (HR) of Incident Diabetes by Frequency of Any Vasomotor Symptoms (VMS) at Baseline and Time-Varying and by VMS Trajectories^a^,^b.

Discussion

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Cite

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PERMALINK

Vasomotor Symptom Trajectories and Risk of Incident Diabetes

Monique M Hedderson, PhD

Emily F Liu, MPH

Catherine Lee, PhD

Samar R El Khoudary, PhD

Ellen B Gold, PhD

Carol A Derby, PhD

Rebecca C Thurston, PhD

Abstract

Introduction

Methods

Results

Table 1. Baseline Characteristics of Study Sample From Study of Women’s Health Across the Nation Population.

Table 2. Crude and Adjusted Hazard Ratio (HR) of Incident Diabetes by Frequency of Any Vasomotor Symptoms (VMS) at Baseline and Time-Varying and by VMS Trajectoriesa,b.

Discussion

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Table 2. Crude and Adjusted Hazard Ratio (HR) of Incident Diabetes by Frequency of Any Vasomotor Symptoms (VMS) at Baseline and Time-Varying and by VMS Trajectories^a^,^b.