Extent of Abdominal Aortic Calcification Is Associated With Incident Rapid Weight Loss Over 5 Years: The Perth Longitudinal Study of Ageing Women
Arteriosclerosis, Thrombosis, and Vascular Biology
Abstract
BACKGROUND:
Abdominal aortic calcification (AAC), a marker of vascular disease, is associated with disease in other vascular beds including gastrointestinal arteries. We investigated whether AAC is related to rapid weight loss over 5 years and whether rapid weight loss is associated with 9.5-year all-cause mortality in community-dwelling older women.
METHODS:
Lateral spine images from dual-energy x-ray absorptiometry (1998/1999) were used to assess AAC (24-point AAC scoring method) in 929 older women. Over 5 years, body weight was assessed at 12-month intervals. Rapid weight loss was defined as >5% decrease in body weight within any 12-month interval. Multivariable-adjusted logistic regression was used to assess AAC and rapid weight loss and Cox regression to assess the relationship between rapid weight loss and 9.5-year all-cause mortality.
RESULTS:
Mean±SD age of women was 75.0±2.6 years. During the initial 5 years, 366 (39%) women presented with rapid weight loss. Compared with women with low AAC (24-point AAC score 0–1), those with moderate (24-point AAC score 2–5: odds ratio, 1.36 [95% CI, 1.00–1.85]) and extensive (24-point AAC score 6+: odds ratio, 1.59 [95% CI, 1.10–2.31]) AAC had higher odds for presenting with rapid weight loss. Results remained similar after further adjustment for dietary factors (alcohol, protein, fat, and carbohydrates), diet quality, blood pressure, and cholesterol measures. The estimates were similar in subgroups of women who met protein intake (n=599) and physical activity (n=735) recommendations (extensive AAC: odds ratios, 1.81 [95% CI, 1.12–2.92] and 1.58 [95% CI, 1.02–2.44], respectively). Rapid weight loss was associated with all-cause mortality over the next 9.5 years (hazard ratio, 1.49 [95% CI, 1.17–1.89]; P=0.001).
CONCLUSIONS:
AAC extent was associated with greater risk for rapid weight loss over 5 years in older women, a risk for all-cause mortality. Since the association was unchanged after taking nutritional intakes into account, these data support the possibility that vascular disease may play a role in the maintenance of body weight.
Graphical Abstract
Highlights
•
Rapid weight loss is common in older women and associated with poorer prognosis.
•
We demonstrate that older women with moderate to extensive abdominal aortic calcification—a marker of advanced vascular disease—have greater risk for rapid weight loss over 5 years.
•
This relationship remained even in people meeting the protein and physical activity recommendations to maintain body weight, suggesting the need for new strategies in older people with structural vascular disease.
Rapid weight loss is generally defined as an involuntary decline in total body weight by >5% over a 6- to 12-month period.1 Some have considered this magnitude of weight loss to be an indicator of health status, occurring in up to 27% of community-dwelling frail older adults.2 While the pathophysiology of rapid weight loss is poorly understood,1,3 it is associated with poorer health outcomes, such as an increased risk of falls, fractures, mortality, institutionalization, cognitive decline, Alzheimer disease, and declines in physical function and quality of life.1,4,5 Related syndromes include cachexia, a multifactorial, complex, and debilitating syndrome typically presenting as a part of advanced chronic disease (cancer, kidney disease, and heart failure) and characterized by loss of body weight, encompassing both muscle and adipose tissue and possibly bone loss.6,7 While all patients with cachexia have rapid weight loss, not all patients with rapid weight loss have cachexia. The increase in protein catabolism that presents with cachexia cannot usually be reversed by diet alone. Perhaps rapid weight loss may be a preclinical marker to indicate a need for early interventions that may improve clinical disease course and quality of life for such high-risk individuals.8 Notably, a link between vascular health and the absorption of nutrients at the gut has been raised,9 suggesting a nexus between compromised vascular blood supply and suboptimal nutrient absorption.
Abdominal aortic calcification (AAC)—a process involving mineral deposition in the arterial wall—represents an early site of vascular calcification and a marker of multisite atherosclerosis and clinical cardiovascular disease (CVD) risk. AAC can be assessed relatively easily from lateral spine images taken at the time of a bone density assessment.10 The presence of AAC has been shown to predict future risk for cardiovascular events, poor prognosis, and all-cause mortality.11 We have previously reported that older women who had extensive AAC presented with a greater decline in grip strength.12 Others have reported that older men had higher risk of AAC progression if they had low muscle mass and poorer physical function.13 We have also shown that the extent of AAC in older women is related to late-life dementia,14 as well as increased fall and fracture risk.15,16 Such events and declines in musculoskeletal health are also known risk factors of rapid weight loss.1,4,5
The presence and extent of AAC has been shown to be related to atherosclerosis and stenosis of other vascular beds including the superior mesenteric arteries (which branch off from the aorta and supply blood to the gastrointestinal tract) and is related to all-cause mortality.11,17–19 Furthermore, the extent of AAC has been shown to be related to other organs including associations with common bile duct diameter,20 which is important for nutrient absorption. This suggests that generalized atherosclerosis may also effect the functioning of other intra-abdominal organs. Risk of atherosclerotic plaque buildup of the arteries supplying the gastrointestinal tract (celiac, superior mesenteric, and inferior mesenteric arteries) increases with increasing age18 and can result in postprandial abdominal pain and weight loss.21 Therefore, we hypothesized that AAC, a potential marker of generalized ischemic gastrointestinal artery disease, particularly of the mesenteric arteries, is related to rapid weight loss.
This study examined the association between the extent of AAC and the risk for rapid weight loss, over 5 years in community-dwelling older women. A secondary aim was to examine whether women who experienced rapid weight loss had increased risk for all-cause mortality in the following 9.5 years.
METHODS
Ethical Guideline Statement
All participants provided written informed consent. Ethics approval was granted by the Human Research Ethics Committee of the University of Western Australia. CAIFOS and PLSAW complied with the Declaration of Helsinki and were retrospectively registered with the Australian New Zealand Clinical Trial Registry (CAIFOS: URL: https://www.anzctr.org.au; unique identifier: ACTRN12615000750583. PLSAW: URL: https://www.anzctr.org.au; unique identifier: ACTRN12617000640303). All study procedures complied with the Declaration of Helsinki. Human ethics approval for the use of linked data was approved by the Human Research Ethics Committee of the Western Australian Department of Health (project number 2009/24).
Study Population
The data that support the findings of this study are available from the corresponding author upon reasonable request and in-line with governing ethical considerations (Major Resources Table). The study population included women from PLSAW (Perth Longitudinal Study of Ageing in Women; http://www.lsaw.com.au). Women who had an expected survival beyond 5 years were recruited to the 5-year double-blind, randomized controlled trial, CAIFOS (Calcium Intake and Fracture Outcome Study), where women were provided with a daily calcium supplement or placebo.22 Women (n=1500) were recruited from the Western Australian general population electoral roll, and among them, 1053 women had AAC assessed from lateral spine imaging in 1998 to 1999 (Figure S1). Women without an AAC assessment (n=447/1500) or without annual weight assessment over the 5-year period (n=109/946) were not included in the analysis. An additional 8 women were excluded who had missing covariates (smoking and energy intake) and 7 removed who had implausible energy intakes of >14 700 kJ/d (n=7). As a result, the current study included 929 women.
Baseline Assessments
At baseline, physical activity and smoking questionnaires were completed. As described previously,23 participants were asked to report on their participation in sport, recreation, and physical activity undertaken in the 3 months before their baseline visit. In brief, the level of primary activity, expressed in kilocalories expended per day, was calculated from the questionnaire using a validated method that takes into consideration the type of activity, time spent performing the activity, and the participants’ body weight.24 Smoking status was coded as nonsmoker or smoker, where smoker was defined as a 3-month period where a participant had consumed >1 cigarette daily at any point in their lifetime. Participants’ body weights (kg) were assessed using calibrated, digital scales without footwear and in light clothing, and height (cm) was assessed using a stadiometer. Body mass index (BMI) was then calculated as kilograms per square meter. Blood pressure was measured on the right arm after a 5-minute resting period sitting in an upright position with a mercury column manometer (adult cuff). An average of 3 blood pressure readings was recorded. Pulse pressure was then calculated as systolic blood pressure minus diastolic blood pressure.
Participants provided their medical history and current medication use, which was then verified where possible by their general practitioner. The data were then coded using the Internal Classification of Primary Care-Plus method,25 allowing aggregation of different terms for similar pathological entities as defined by the International Classification of Diseases (ICD), Tenth Revision, coding system.25 Diabetes prevalence was then determined based on these data.26 Medications included antihypertensives, statins, and low-dose aspirin use. The ICD, Ninth Revision, Clinical Modification, codes to determine prevalence of disease for the period 1980 to 1998 were as follows: prevalent CVD (ICD, Ninth Revision, Clinical Modification, codes 390–459), prevalent chronic obstructive pulmonary disease (ICD, Ninth Revision, Clinical Modification, codes 490–496), and prevalent cancer (ICD, Ninth Revision, Clinical Modification, codes 140–239 excluding 210–229). Estimated glomerular filtration rate was calculated using the creatinine-based Chronic Kidney Disease Epidemiology Collaboration equation and was used to identify women with/without chronic kidney disease (CKD; estimated glomerular filtration rate rate, <60 mL/min per 1.73 m2), as described previously.27
Dietary Assessment
Assessment of dietary intake has been described in detail previously.28 In brief, it was assessed at baseline (1998) and at 5 years (2003) according to a self-administered, semiquantitative Food Frequency Questionnaire, developed and validated by the Cancer Council of Victoria.29,30 Energy intake, energy (kJ/d), and nutrient intake were calculated by the Cancer Council of Victoria primarily using the NUTTAB95 food composition database.31 Protein intake was calculated as grams per day. Overall diet quality was assessed using the Nutrient Rich Foods Index calculated using nutrient density scores and was adapted using nutrient reference values for Australian and New Zealand adult women >70 years of age, as described previously for this study cohort.32
Assessment of AAC
Lateral spine images for AAC assessment were captured at the time of dual-energy x-ray absorptiometry (DXA) from 1998 (baseline) or 1999 (year 1). AAC was scored using the 24-point scoring method (AAC-24), derived from digitally enhanced lateral single-energy images of the thoracolumbar spine, obtained using the Hologic 4500A bone densitometer (Hologic, Bedford, MA). All images were assessed by a single experienced investigator (J.T.S.) using a validated semiquantitative scoring system, detailed previously.33 The AAC-24 scoring system grades AAC relative to each vertebral height (L1–L4). Each vertebrae is scored as 0 (no calcification), 1 (≤1/3 of the aortic wall), 2 (>1/3 to ≤2/3 of the aortic wall), or 3 (>2/3 of the aortic wall) for both anterior and posterior aortic walls.34 The maximum possible score is 24 (extensive calcification). Participants were stratified based on the AAC-24 score into low AAC (0–1), moderate AAC (2–5), and extensive AAC (≥6) based on established clinically relevant thresholds.35
Assessment of Rapid Weight Loss
Body weight was assessed in kilograms on 6 occasions at each 12-month interval at baseline to 5 years. Incident rapid weight loss was defined as >5% decrease in body weight in any 12-month period.1
Mortality Outcomes
For each participant, mortality records were obtained from the Western Australian Data Linkage System, Department of Health Western Australia, East Perth, Australia. Records were obtained and verified for all study participants from 5 years after the date of their baseline clinic visit for a further 9.5 years. Codes were identified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification. The primary outcome of this analysis was all-cause mortality. Additional outcomes included primary cause of death CVD (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification, codes I00–I99), cancer death (codes included International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification, C00–D48 excluding D10–D36), and other deaths (all other codes). All deaths were defined using part 1 of the death certificate or primary cause of death diagnosis text field of the death certificate was used where coded data were not yet available to ascertain the cause of death. Two deaths were under coronial inquiry at the time of data extraction, and no primary cause of death was available.
Biochemistry
Fasting blood samples were obtained at baseline in 1998. Total cholesterol and HDL (high-density lipoprotein) cholesterol were measured using a Hitachi 917 auto analyzer (Roche Diagnostics GmbH, Mannheim, Germany). LDL (low-density lipoprotein) cholesterol was calculated using the Friedewald method.36 In 2019, a subset (n=405) of these samples was reanalyzed using the Abbott ARCHITECT ci16200 Integrated System. In this subset, for majority of women, LDL was calculated using the Friedewald method36; however, if triglycerides were >4.5 mmol/L, it was measured. When combining cholesterol data, we preferentially used the 2019 values. For all analyses, where cholesterol measurement was used as an adjustment factor, the variable time was additionally adjusted for in the model to account for differences in time, or assay.
Statistical Analysis
Baseline data are presented as either mean±SD, median and interquartile range, or number and percentage where appropriate. The primary outcome of the study was rapid weight loss while the category of AAC severity was the primary exposure variable. AAC was examined as a categorical (low, moderate, and extensive AAC) variable in the models. Logistic regression was used to determine the association between AAC groupings and odds for rapid weight loss adjusted using 2 models. The 2 models of adjustment were as follows: model 1: baseline age, BMI, and treatment (calcium or placebo) during the 5 years; and model 2: model 1 plus baseline prevalent diabetes (yes/no), prevalent CVD (yes/no), smoking history (yes/no), use of aspirin (yes/no), statins (yes/no), antihypertensive (yes/no), energy intake (kJ/d), and physical activity (kcal/d).
Additional Analysis
In additional analyses, we explored the relationship of rapid weight loss with 9.5-year all-cause, CVD, cancer, and other mortality using the multivariable-adjusted Cox regression model. Kaplan-Meier curves and the log-rank test were also used to assess this relationship. To exclude women potentially trying to lose weight, we performed the same Cox regression after excluding women with a BMI ≥30 kg/m2. The aforementioned analyses were repeated using a threshold for rapid weight loss at >10% within any 1-year period as a >5% loss of body weight within 6 to 12 months is generally accepted as rapid weight loss.1 To consider the impact of other risk factors for rapid weight loss (alcohol [g/d], protein [g/d], total fat [g/d], and carbohydrate intake [g/d]; diet quality [Nutrient Rich Foods Index]; weakness as indicated by hand grip strength [kg]; low mobility assessed by a timed up-and-go test; pulse pressure; blood pressure [systolic and diastolic]; total, HDL, and LDL cholesterol; as well as prevalent cancer and CKD), we entered these variables into separate multivariable-adjusted regression models. To further investigate the nature of the relation between AAC and rapid weight loss, we performed additional subgroup analyses. In our subgroup analyses, we undertook the primary analysis between AAC and rapid weight loss in women who (1) met (sufficient; ≥1.0 g/kg) or did not meet (insufficient) protein intake recommendations,37 (2) met (>150 min/wk was considered adequate38) or did not meet physical activity recommendations, (3) did or did not present with chronic disease (prevalent chronic disease includes CVD, CKD, and chronic obstructive pulmonary disease), and (4) had a normal or overweight BMI at baseline <30 kg/m2 or were classified as obese ≥30 kg/m2.
Using the different threshold of rapid weight loss at >10%, we repeated the primary analyses using logistic regression to explore the association of AAC groups and risk for rapid weight loss, adjusted using the multivariable model. Using linear regression, we explored whether the change in weight over the 5 years (difference between year 5 and baseline, kg) was related to AAC-24 at baseline, adjusted using the multivariable model, using baseline weight and height instead of baseline BMI as a covariate. Finally, to assess for the potential of reverse causality bias, we performed a sensitivity analysis excluding women who had experienced rapid weight loss within the first 2 years of follow-up.
To explore trajectories of weight change over the 5-year period, we used the user-defined Stata command, traj, for fitting a finite (discrete) mixture model to the longitudinal trajectory of percentage weight change to identify clusters of individuals following similar progressions of weight-change time. We used models with between 3 and 6 clusters and identified the optimal model using the lowest AIC (Akaike information criterion) and BIC (Bayesian information criterion) statistics. A model with 3 groups of weight-change trajectories was determined to be the best fitting model (BIC, −12882.53; AIC, −12824.52). Using these 3 groups of weight-change trajectories, all women who had a rapid weight loss event at any time were then grouped within each class by the period they experienced the rapid weight loss, and the mean weight for each trajectory was plotted (eg, 0–12 and 12–24 months).
For statistical analysis, a combination of IBM Statistical Package for the Social Sciences (version 25.0; IBM Corp, Armonk, NY) and R (version 3.4.2; R Foundation for Statistical Computing, Vienna, Austria)39 was used. Statistical significance was determined at P<0.05 for all tests.
RESULTS
Baseline characteristics for all women, stratified by the presence and absence of rapid weight loss over the 5-year period, are presented in Table 1. The mean±SD age of the participants was 75.0±2.6 years and BMI was 27.1±4.4 kg/m2. Compared with women with stable weight, those who experienced rapid weight loss were more likely at baseline to have a higher body weight and BMI, to be taking aspirin medications, and to have poorer mobility (slower time to complete the timed up-and-go test).
| All participants (N=929) | Rapid weight loss (n=366) | No rapid weight loss (n=563) | P value | |
|---|---|---|---|---|
| Age, y | 75.0±2.6 | 75.1±2.6 | 74.9±2.6 | 0.443 |
| Calcium supplementation group, n (%) | 450 (48.4) | 176 (48.1) | 274 (48.7) | 0.863 |
| Body weight, kg | 68.6±11.9 | 70.2±12.7 | 67.5±11.2 | 0.001 |
| Height, cm | 159.2 (155.2–163.0) | 159.3 (155.2–163.0) | 159.2 (155.3–163.0) | 0.975 |
| BMI, kg/m2 | 27.1±4.4 | 27.7±4.7 | 26.7±4.2 | <0.001 |
| Pulse pressure,* mm Hg | 64±16 | 65±16 | 64±16 | 0.441 |
| Systolic blood pressure, mm Hg | 138±18 | 139±19 | 137±18 | 0.103 |
| Diastolic blood pressure, mm Hg | 73±11 | 74±11 | 73±10 | 0.103 |
| Total cholesterol, mg/dL† | 224.8±41.0 | 225.7±40.2 | 224.2±41.5 | 0.597 |
| LDL, mg/dL | 140.5±36.5 | 140.9±34.8 | 140.3±37.5 | 0.831 |
| HDL, mg/dL | 57.6±13.8 | 57.2±13.9 | 57.8±13.7 | 0.516 |
| Ever smoked, yes, n (%) | 331 (35.6) | 127 (34.7) | 204 (36.2) | 0.633 |
| Diabetes, yes, n (%) | 54 (5.8) | 19 (5.2) | 35 (6.2) | 0.514 |
| Statins use, yes, n (%) | 174 (18.7) | 68 (18.6) | 106 (18.8) | 0.924 |
| Aspirin use, yes, n (%) | 188 (20.2) | 91 (24.9) | 97 (17.2) | 0.005 |
| Antihypertensive use, yes, n (%) | 401 (43.2) | 168 (45.9) | 233 (41.4) | 0.174 |
| CKD, yes, n (%) | 267 (31.8) | 104 (31.7) | 163 (31.8) | 0.969 |
| CVD, yes, n (%) | 219 (23.6) | 93 (25.4) | 126 (22.4) | 0.288 |
| Chronic obstructive pulmonary disease, yes, n (%) | 10 (1.1) | 3 (0.8) | 7 (1.2) | 0.541 |
| Prevalent any cancer | 55 (5.9) | 17 (4.6) | 38 (6.7) | 0.184 |
| Physical function | ||||
| Physical activity, kcal expended per day | 118 (46–205) | 113 (39–207) | 119 (52–204) | 0.197 |
| Sedentary, n (%) | 194 (20.9) | 85 (23.2) | 109 (19.4) | 0.157 |
| Grip strength, kg | 20.8±4.6 | 20.5±4.7 | 20.9±4.5 | 0.111 |
| Timed up and go, s | 9.2 (8.0–10.7) | 9.4 (8.2–10.8) | 9.0 (7.9–10.5) | 0.001 |
| Nutritional measures | ||||
| Energy intake, kJ/d | 7154±2059 | 7115±2120 | 7179±2020 | 0.642 |
| Protein intake, g/d | 76.5 (61.5–94.2) | 76.2 (60.7–93.6) | 77.3 (62.0–94.8) | 0.456 |
| Total fat intake, g/d | 64.6±23.1 | 65.2±24.6 | 64.2±22.0 | 0.522 |
| Carbohydrate intake, g/d | 192.1±57.0 | 188.9±56.2 | 194.2±57.5 | 0.168 |
| Alcohol intake, g/d | 1.9 (0.3–9.4) | 1.9 (0.3–10.1) | 2.0 (0.4–9.0) | 0.633 |
| Nutrient Rich Foods Index | 75.8±24.7 | 76.4±26.1 | 75.5±23.7 | 0.561 |
| Abdominal aortic calcification | 0.041 | |||
| Low (AAC-24 0–1) | 417 (44.9) | 146 (39.9) | 271 (48.1) | |
| Moderate (AAC-24 2–5) | 328 (35.3) | 138 (37.7) | 190 (33.7) | |
| Extensive (AAC-24 6+) | 184 (19.8) | 82 (22.4) | 102 (18.1) | |
Data are presented as mean±SD, median (interquartile range) for non-normally distributed variables, or n (%) for categorical variables. Pulse pressure was calculated as systolic minus diastolic pressure. Independent samples t test was used to test for significant difference between groups with the chi-square test used for categorical variables where indicated. AAC-24 indicates 24-point abdominal aortic calcification score; BMI, body mass index; CKD, chronic kidney disease; CVD, cardiovascular disease; HDL, high-density lipoprotein; and LDL, low-density lipoprotein.
*
Total sample size for blood pressure measurements was n=904 (n=354, rapid weight loss group; n=550, no rapid weight loss group).
†
Cholesterol was measured in 1998 or 2019. Total sample size for total cholesterol and HDL is n=863 (n=335, rapid weight loss group; n=528, no rapid weight loss group) and for LDL is n=858 (n=331, rapid weight loss group; n=527, no rapid weight loss group).
Rapid Weight Loss
Over the 5-year follow-up period (n=929), 366 (39.4%) women experienced rapid weight loss, with incident rapid weight loss in any 1-year period ranging between 8.7% and 10.9%. Over 5 years, of the women experiencing rapid weight loss, 302 (82.5%) women experienced rapid weight loss in a single year, 56 (15.3%) experienced rapid weight loss in 2 of the 5 years, 6 (1.6%) experienced rapid weight loss in 3 of the 5 years, and 2 (0.5%) women experienced rapid weight loss in 4 of the 5 years. Rapid weight loss over 5 years was associated with 49% increased risk for 9.5-year all-cause mortality, and 62% and 57% increased risk for CVD and other mortality, adjusted using the multivariable model (Table S1; Figure S2). When excluding women with a BMI of >30 kg/m2 (n=721), the association between rapid weight loss and 9.5-year all-cause mortality increased to 58% (Table S1). When exploring the threshold of rapid weight loss at >10%, the association between rapid weight loss and all-cause mortality was even higher, increasing to 136% (Table S1; Figure S3, respectively).
Extent of AAC and 5-Year Odds for Rapid Weight Loss
In the minimally adjusted model, compared with women with low AAC, women with moderate and extensive AAC have 36% and 58% higher odds, respectively, of having rapid weight loss over 5 years (Table 2). Point estimates remained similar in the multivariable adjusted model.
| Rapid weight loss | Events | Model 1, OR (95% CI) | Model 2, OR (95% CI) |
|---|---|---|---|
| AAC groups | |||
| Low (0 or 1) | 146/417 (35.0) | 1.0 Ref | 1.0 Ref |
| Moderate (2–5) | 138/328 (42.1) | 1.36 (1.01–1.83)* | 1.36 (1.00–1.85)* |
| Extensive (≥6) | 82/184 (44.6) | 1.58 (1.10–2.26)* | 1.59 (1.10–2.31)* |
Model 1: age, BMI, and treatment (calcium/placebo) adjusted. Model 2: model 1 plus diabetes, CVD, smoking history, use of aspirin, statins, antihypertensive, energy intake, and physical activity. AAC-24 scores of 0 or 1, 2 to 5, and 6+ were used to categorize low, moderate, and extensive AAC, respectively. AAC indicates abdominal aortic calcification; AAC-24, 24-point abdominal aortic calcification score; BMI, body mass index; CVD, cardiovascular disease; OR, odds ratio; and Ref, reference.
*
P<0.05 compared with reference.
Additional Analyses
In separate multivariable-adjusted analyses, the individual addition of alcohol, protein, total fat and carbohydrate intake, Nutrient Rich Foods Index, grip strength, and timed up-and-go performance, pulse pressure, blood pressure (systolic and diastolic), prevalent cancer, or CKD did not substantively change the results for the extensive AAC group (Table S2). When adding lipids (total, HDL, and LDL cholesterol) as additional covariates into the model, there was an attenuation of ≈11% to 20% for the point estimates (Table S2). These additional variables were not significant in the models when AAC was removed.
As adequate protein intake and physical activity are common strategies to optimize body composition in older adults,40,41 we undertook subgroup analyses based on this a priori hypothesis where women were stratified based on having met protein or physical activity recommendations. Based on these subgroupings, 599 (64.3%) women had sufficient protein intake, while 735 (79.1%) performed sufficient physical activity. In the subgroup that included only women who met protein intake recommendations, 221 (36.9%) of these women had a rapid weight loss event. In the multivariable-adjusted model, compared with women with low AAC, those with moderate or extensive AAC had 69% and 81% higher odds for rapid weight loss over 5 years, respectively (Table S3). In the subgroup of women who met physical activity guidelines levels, 281 (38.2%) of these women had experienced rapid weight loss. Compared with women with low AAC, those with extensive AAC had 58% higher odds for a rapid weight loss event over 5 years (Table S3).
We also considered subgroups of women who had prevalent chronic disease or did not have prevalent chronic disease at baseline (n=521; Table S3). In women with prevalent chronic disease (n=408), there were 166 (40.7%) rapid weight loss events. In this subgroup, compared with women with low AAC, those women with extensive AAC had 75% higher odds for a rapid weight loss event over the next 5 years.
Lastly, women were grouped as having normal or overweight BMI at baseline (n=721) or as obese (n=208; Table S3). In women with normal to overweight baseline BMI, there were 260 (36.1%) rapid weight loss events. The estimate for the relationship between extensive AAC and odds of rapid weight loss remained similar for the normal-overweight BMI group but was no longer significant for obese women. We then performed interaction testing, and the observed differences across subgroups were only significant for the chronic disease versus no chronic disease subgroup (P=0.02).
We also investigated the change in energy intake from baseline to 5 years, adjusted for age, baseline BMI, and baseline energy intake; however, this was not different between AAC groups (low, moderate, and extensive) or in those who did or did not have a rapid weight loss event (all P>0.05).
We explored rapid weight loss at a threshold of >10% as this has been used throughout the literature. At the threshold of >10%, the estimate for rapid weight loss was even greater for the moderate and extensive AAC groups (115% and 118%, respectively; Table S4).
In further analysis, we also ran the weight change over 5 years (difference between year 5 and baseline, kg) and the relationship with AAC-24 adjusted using the multivariable model; baseline weight and height instead of BMI were included here as covariates. AAC-24 was associated with overall weight change (n=875; β=−0.117; P=0.02). To determine whether this weight change was driven by rapid weight loss, we then excluded women who had a rapid weight loss event (n=325). In women without rapid weight loss, there was no association between AAC with overall weight change (n=551; β=−0.025; P=0.62).
To assess the potential of reverse causality bias, we excluded rapid weight loss events that occurred within the first 2 years (n=172/929; 18.5%) and re-ran the analysis. After excluding these cases (n=194/912; 21.3%), the estimates remained similar. Further, when compared with women with low AAC, those with extensive AAC had 68% higher odds of a rapid weight loss event in years 3 to 5 (Table S5). We also then explored women who had a rapid weight loss event in the first 2 years versus those who had a rapid weight loss event in the last 3 years and the association with all-cause and CVD mortality. There was no relationship between women who had a rapid weight loss event in the first 2 years with all-cause mortality (hazard ratio, 0.91 [95% CI, 0.66–1.24]) or CVD mortality (hazard ratio, 0.71 [95% CI, 0.42–1.19]). Women who experienced rapid weight loss in the last 3 years had increased risk for all-cause and CVD mortality (hazard ratios, 1.87 [95% CI, 1.44–2.43] and 2.37 [95% CI, 1.61–3.50]).
When exploring weight trajectories over the 5-year period for the whole study cohort, the model with 3 groups of weight change consisted of group 1 (n=847; 90.1%) that stayed relatively stable in weight throughout the 5 years, group 2 (n=49; 6.3%) that fell in weight in the second 12 months, and then increased back to their original weight in the next 36 months, and group 3 (n=33; 3.6%) that decreased in weight for the first 3 years but then rapidly regained weight in the last 2 years to finish beyond their original weight (Figure S4). Of the 366 women who experienced a rapid weight loss event, 292 (79.8%) of these women were in group 1, 47 (12.8%) in group 2, and 27 (7.4%) in group 3 (Figure S5).
For those in group 1, 87 women experienced a rapid weight loss event in the first year of follow-up, 17 in the second year, 70 in the third year, 57 in the fourth year, and 61 in the fifth year of follow-up. Irrespective of the year of follow-up, for these women, it appears that weight remained relatively stable over the remaining follow-up period but with slightly lower weight compared with baseline at 5 years.
For those in group 2, 3 women had a rapid weight loss event in the first year of follow-up with a continued decline in body weight before then stabilizing and 44 women had a rapid weight loss event in the second year with weight remaining relatively stable thereafter.
Finally, in group 3, 8 women had a rapid weight loss event in the first year of follow-up with weight declining at the second year before then stabilizing; 13 women had an event in the second year with weight stabilizing at the third year increasing above baseline in year 4 and then reducing below baseline in year 5; 4 women had an event in the third year of follow-up with weight stabilizing and then increasing above baseline in year 5; and 2 women had an event in the fourth year with weight then increasing above baseline in year 5.
DISCUSSION
The main finding from this study was that compared with low AAC, women with moderate and extensive AAC had increased risk for rapid weight loss over 5 years in community-dwelling older women. Those who experienced a rapid weight loss event were associated with increased risk of 9.5-year all-cause, CVD, and other mortality. Notably, the risk for all-cause mortality was 87% higher in those who experienced a rapid weight loss of >10%. In our primary analyses where we adjusted for a range of dietary factors (including the overall diet quality using the Nutrient Rich Foods Index) and physical activity, our data suggest that the association between AAC, particularly those with extensive disease, and risk for rapid weight loss may not be explained by such lifestyle factors and requires further investigation. However, we cannot exclude the possibility of unmeasured confounders that may explain these associations. In this study, we have utilized a novel marker of advanced structural vascular disease, AAC, as a potential risk factor for rapid weight loss.
Explanations for the relationship between AAC and rapid weight loss remain unclear. The presence of AAC (via computed tomography or DXA) has been shown to be a marker of generalized atherosclerosis at other vascular beds including at the coronary and carotid arteries.34,42,43 In adults with no known manifest CVD, AAC was related to calcification in other arterial beds, including those essential for gastrointestinal function such as the superior mesenteric, celiac trunk, coronaries, and iliac arteries.44 Further, patients who have diagnosed chronic mesenteric ischemia typically present with weight loss, possibly related to reduced nutrient absorption and blood flow to the intestines.21 Whether these patients also have AAC, to our knowledge, is unknown. Malabsorption and bowel permeability have been discussed in the concept of the pathophysiology of cardiac cachexia.45 This raises the question whether the alteration in nutrient absorption could partly be explained by reduced blood flow to the gut because of mesenteric artery ischemia, including the small/large intestine, which is highly vascularized.46 Others have shown that AAC is related to function of other organs, including the common bile duct diameter,20 which plays an important role in nutrient absorption, suggesting generalized atherosclerosis may affect other intra-abdominal organs. Future studies should explore this hypothesis, investigating whether AAC is associated with gastrointestinal blood flow of the celiac/mesenteric arteries and gastrointestinal complaints consistent with mesenteric ischemia (eg, nausea, abdominal pain). Further, in our additional analyses, the relationship between AAC and rapid weight loss was somewhat attenuated when adding lipids into the multivariable model. This suggests the association between AAC and rapid weight loss may not be independent of lipids and atherogenesis, warranting further investigation in larger data sets.
The concept that vascular dysfunction (ie, stiffness, endothelial dysfunction) may be a precursor to the onset of sarcopenia (low muscle mass, strength, or function) has been raised,47,48 which commonly coexists with CVD and diabetes.40 Whether AAC is a marker of disease within vascular beds contributing to these phenotypes is not clear; yet, worsening AAC has been associated to increased fall risk,16 changes in lower relative lean body mass,49 poorer grip strength and predicts its 5-year decline.12,50 In addition to muscle, given that adipose tissue and bone are highly vascularized organs, any alteration to blood flow to these organs may also affect their optimal functioning51,52 and may contribute to such phenotypes. Nevertheless, using AAC as a potential preclinical marker to identify women at a higher risk for future rapid weight loss could be an easy–to-implement screening strategy. This is particularly relevant with recent automated algorithms capable of rapidly assessing AAC from commonly used bone density machines.53
Traditional management of maintaining body composition of the older adult usually incorporates a lifestyle approach (eg, exercise and healthy diet).40,41 Yet, to our knowledge, our data are the first to suggest that rapid weight loss in these women who have AAC may be occurring independently of protein/energy intake. Consequently, supplementing these older women with higher dietary protein or interventions to increase daily energy intakes may not lower the risk of rapid weight loss. Further studies are needed to confirm this hypothesis by using labeled isotopes to track macronutrient absorption in such populations54 or using more detailed energy intake data given known limitations of using a self-reported questionnaire.55
Physical activity is considered as a cornerstone intervention to prevent, manage, and treat chronic diseases, including CVD, as well as maintain a balanced body weight.56 Yet, our data suggest that even women who met the physical activity recommendations were not protected against rapid weight loss if AAC was extensive. Albeit, as used in the current study, there are known barriers to self-reported physical activity57; therefore, different characteristics of exercise should be explored. Progressive resistance training has good functional outcomes even in patients with conditions of muscle wasting, for example, cachexia.58,59 Little is known about the effects of exercise on AAC. One study in patients with CKD (mean age, 67 years) saw no effect on AAC after 12 months of exercise training; a relatively short period.60 Nonetheless, exercise has a plethora of benefits including at the vasculature, that is, improving endothelial-dependent vasodilation in sedentary individuals61 and gut, that is, improves microbiome diversity62 and gut motility.63 Studies are needed to determine whether exercise can improve arterial blood flow to the gastrointestinal tract and prevents rapid weight loss better than a dietary intervention alone.
Autopsy and imaging studies report that the iliac artery bifurcation and the abdominal aorta are often the first sites where vascular calcification is seen.64,65 While AAC is common in older adults, it is not normal. A recent meta-analysis reported that high-risk populations, such as older adults (median age, 68 years) and those with a chronic disease (eg, CKD) with any or more extensive AAC have higher risk for cardiovascular events, fatal cardiovascular events, and all-cause mortality compared with those with no AAC.11 A systematic review and meta-analysis of observational studies (n=178 644 people) reports that individuals who experience a rapid weight loss event have 23% higher risk for all-cause mortality, increasing to 81% in older adults.66 Our data support these findings showing that women who experienced a rapid weight loss event had 49% increased risk for all-cause mortality over 9.5 years. In an attempt to minimize any influence of those intentionally trying to lose weight, we performed the same analysis excluding women with a BMI >30 kg/m2; a greater hazard was observed (increase from 49% to 58%). Although women of lower BMIs may still have tried to intentionally lose weight, this should be explored in future. The increase in risk for 9.5-year all-cause mortality to 136% at the higher threshold of weight loss, at >10%, may suggest the increased need for further investigation in such patients. We also explored the underlying cause of death (primary), with our data showing that women who experienced rapid weight loss over 5 years were at increased risk of cardiovascular-related or other causes of death but not cancer. Future work investigating rapid weight loss and mortality outcomes needs to be explored.
Rapid weight loss is also linked to poorer prognosis, including early institutionalization, declines in function and quality of life, and risk for cognitive decline and Alzheimer disease.2,4,5 In our study, women with extensive AAC had lower body weight at baseline, suggesting they may have already lost weight and the capacity to lose weight may have been slightly diminished. However, these individuals presented with the highest odds for rapid weight loss. Because vascular calcification (moderate or extensive AAC) did not increase the risk of rapid weight loss in obese women, this may be best applied to nonobese women when assessing risk for rapid weight loss. Rapid weight loss could be considered as a preclinical marker to identify older women at risk of poorer health outcomes (ie, all-cause mortality) early and is a simple and cost-effective screening strategy that could be incorporated into routine clinical assessment. Clinically, when patients present with weight loss, clinicians will investigate, for example, malignancy, cachexia, malnutrition or malabsorption syndromes, anxiety or depression, or postprandial angina, to exclude different pathologies. As part of bone density screening, AAC represents an opportunity to screen for individuals at increased risk for future rapid weight loss in conjunction with evidence for preclinical CVD (ie, generalized atherosclerosis of other vascular beds), which may warrant further investigation.
This study does have limitations. Due to the observational nature, causality cannot be assumed for the relationship between AAC and rapid weight loss. A limitation of this work was that we could not confirm whether weight loss was unintentional. However, we tried to account for this by rerunning the primary analyses after excluding women who are obese and it did not affect the relationship. AAC was also assessed using a semiquantitative method, which is operator dependent. We previously reported good interoperator agreement between our highly experienced investigator (J.T.S.) and another experienced investigator with high intraclass correlation coefficients (0.89 [95% CI, 0.80–0.94]) for AAC scores.67 Lateral spine imaging via DXA represents a safe, reliable, and low-cost approach to assess AAC, particularly given lateral spine images have already been incorporated into osteoporosis screening.68 Currently, there is great interest in the development of machine learning algorithms that can automatically assess AAC, which will substantially improve its clinical utility.69 Finally, the women in those cohort are predominately White; therefore, findings of this study may not be generalizable to other ethnicities, as well as other clinical populations, or men. The strengths of this study include the examination of the association between AAC and rapid weight loss in a large, prospective cohort of community-dwelling older women. The study also includes weight measurements every 12 months over 5 years, which has allowed us to explore weight trajectories over the 5-year follow-up period using advanced statistical analyses to identify group trends and then further characterize rapid weight loss classes per each 12-month period. We also have detailed nutritional assessments accounting for dietary intake. Lateral spine images using DXA machines were used to quantify AAC, performed in a blinded fashion and scored by a single experienced investigator (J.T.S.), minimizing operator’s bias. This large cohort study also includes detailed information of potential confounders, that is, BMI, prevalent diabetes, CVD history, medication use, and lifestyle were included in the models.
In conclusion, moderate and extensive AAC was associated with greater risk for rapid weight loss over 5 years in community-dwelling older women. Those women who experienced rapid weight loss were at increased risk for all-cause mortality in the following 9.5 years. Our data suggest that the relationship between AAC, particularly those with extensive calcification, and the risk for rapid weight loss is not likely to be explained by dietary intakes of protein and energy or physical activity because the results were not changed by adjustment for these factors. Alternatively, we hypothesize that vascular disease may play a key role by limiting blood flow and nutrient bioavailability. Given the poor prognosis usually associated with rapid weight loss in the older adult, using AAC as a tool to identify those older women with high risk represents an opportunity for not only rigorous CVD risk factor assessment and management but also to consider disease in other vascular beds or organs that can influence body composition.
ARTICLE INFORMATION
Supplemental Material
Figures S1–S5
Tables S1–S5
Major Resources Table
Acknowledgments
The authors wish to thank the staff at the Western Australia Data Linkage Branch, Hospital Morbidity Data Collection, the Australian Coordinating Registry, the State Registries of Births, Deaths and Marriages, the Coroners, the National Coronial Information System, and the Victorian Department of Justice and Community Safety for providing the cause of death unit record file data. C. Smith, M. Sim, and J.R. Lewis conceived and designed the study; R.L. Prince, K. Zhu, and J.R. Lewis acquired funding and collected the data; C. Smith and M. Sim were involved in the data analysis; C. Smith prepared the manuscript with input from all authors; C. Smith and J.R. Lewis had the primary responsibility for the final content. All authors provided intellectual input, edited the manuscript, and approved the final manuscript.
Footnote
Nonstandard Abbreviations and Acronyms
- AAC
- abdominal aortic calcification
- AAC-24
- 24-point abdominal aortic calcification score
- BMI
- body mass index
- CAIFOS
- Calcium Intake and Fracture Outcome Study
- CKD
- chronic kidney disease
- CVD
- cardiovascular disease
- HDL
- high-density lipoprotein
- ICD
- International Classification of Diseases
- LDL
- low-density lipoprotein
- PLSAW
- Perth Longitudinal Study of Ageing Women
Supplemental Material
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© 2023 American Heart Association, Inc.
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Received: 11 September 2023
Accepted: 27 November 2023
Published online: 14 December 2023
Published in print: February 2024
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Disclosures None.
Sources of Funding
PLSAW (Perth Longitudinal Study of Ageing Women) was funded by Healthway, the Western Australian Health Promotion Foundation, and by project grants 254627, 303169, and 572604 from the National Health and Medical Research Council of Australia. J.R. Lewis is supported by the National Heart Foundation of Australia Future Leader Fellowship (ID: 102817), which also supports the salary of C. Smith. M. Sim is supported by a Royal Perth Hospital Career Advancement Fellowship (ID: CAF1302020) and an Emerging Leader Fellowship from the Department of Health (Western Australia). None of these funding agencies had any role in the conduct, collection, management, analysis or interpretation of the data, preparation, review, or approval of the manuscript. D.P. Kiel’s effort was supported by a grant from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01 AR41398).
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- Analysis of the relationship between abdominal aortic calcification and frailty in the middle-aged and older US population, Preventive Medicine Reports, 51, (102994), (2025).https://doi.org/10.1016/j.pmedr.2025.102994
- The Naples prognostic score as a new predictive index of severe abdominal aortic calcification: a population-based study, Frontiers in Cardiovascular Medicine, 12, (2025).https://doi.org/10.3389/fcvm.2025.1545927
- Abdominal Aortic Calcification as a Predictor of Incomplete Adjuvant Chemotherapy in Stage III Colorectal Cancer: A Retrospective Cohort Study, Cureus, (2024).https://doi.org/10.7759/cureus.71288
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