The association of weight change and all-cause mortality in older adults: a systematic review and meta-analysis

Alharbi, Tagrid Abdullah G; Paudel, Susan; Gasevic, Danijela; Ryan, Joanne; Freak‐Poli, Rosanne; Owen, Alice

doi:10.1093/ageing/afaa231

Cited by 51 publications

(52 citation statements)

References 28 publications

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“…In the present study, weight gain was not significantly associated with all-cause mortality risk nor CVD or cancer-specific mortality. This contrasts the findings of meta-analyses, which have reported a modest but significant associations between weight gain and all-cause mortality [ 21 , 28 , 32 ]. This could possibly be impacted by the obesity paradox, supported by epidemiological studies in older adults that overweight and obesity may not affect all-cause or specific cause of mortality [ 33 ].…”

Section: Discussioncontrasting

confidence: 98%

“…The present findings are consistent with the results of previous meta-analyses on the association between weight loss and mortality risk [ 21 , 28 , 32 ]. For example, a recent systematic review and meta-analysis reported that weight loss was associated with a 59% increased mortality risk in adults aged 65 years or over [ 21 ]. Results of another meta-analysis indicated a 67% increase in all-cause mortality risk associated with weight loss among adults aged 60 years or over [ 28 ].…”

Section: Discussionsupporting

confidence: 93%

“…Baseline variables were considered as covariates based on those sufficiently common and widely recognised to contribute to the risk of mortality in older individuals, while at the same time theoretically could contribute to weight change, and not be considered to be on a causal pathway between weight change and mortality. This included demographic factor variables and clinical measures variables [ 21 , 28 ]. In addition, a restricted cubic spline model was used to examine the relationship between continuous percentage weight change and all-cause mortality with five knots: 5th, 25th, 50th, 75th and 95th percentiles using Cox proportional hazards models adjusted for Model 3.…”

Section: Methodsmentioning

confidence: 99%

“…Considering the shifts in body composition distribution in older age, weight change may be a better mortality predictor than a single weight assessment in older adults [ 20 ]. A recent meta-analysis including 30 studies of older adults aged 65 years and over demonstrated that weight loss was associated with a 59% increase in all-cause mortality, while weight gain was associated with a more modest 10% increase in all-cause mortality in the pooled analysis compared to stable weight, and not all studies found weight gain to be associated with increased risk of mortality [ 21 ]. Method of weight measurement (e.g., self-reported versus objectively measured) and length of follow-up were major sources of heterogeneity between studies contributing to the meta-analysis.…”

Section: Introductionmentioning

confidence: 99%

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Objectively Assessed Weight Change and All-Cause Mortality among Community-Dwelling Older People

et al. 2022

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Later life changes in body weight may be associated with an increased risk of mortality in older adults. The objective of this study was to examine whether weight change over four years was associated with a 17-year mortality risk in older adults. Participants were 1664 community-dwelling adults aged ≥65 years in the longitudinal Enquete de Sante’ Psychologique-Risques, Incidence et Traitement (ESPRIT) study. Outcomes were all-cause mortality, cardiovascular disease (CVD) and cancer mortality. Weight change was defined as difference between weight at baseline and 4 years, categorised into: weight stable (±<5% weight change), weight loss (≥5%) and weight gain (≥5%). Association between weight change and mortality risk was evaluated using Cox proportional hazards models. Over 17 years of follow-up (median 15 years), 565 participants died. Compared to stable weight participants, those with ≥ 5% weight loss had an increased risk of all-cause mortality (HR: 1.24, 95% CI: 1.00–1.56, p = 0.05) and CVD mortality (HR: 1.53, 95% CI: 1.10–2.14, p = 0.01), but not cancer mortality (HR: 0.83, 95% CI: 0.50–1.39, p = 0.49). Weight gain of ≥5% was not associated with increased mortality (HR: 1.05, 95% CI: 0.76–1.45, p = 0.74). Weight monitoring in older adults could help identify weight loss at its early stages to better target interventions to maintain nutritional reserve and prevent premature mortality.

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Section: Discussioncontrasting

confidence: 98%

Section: Discussionsupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Objectively Assessed Weight Change and All-Cause Mortality among Community-Dwelling Older People

et al. 2022

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“…For example, a meta-analysis of thirty studies showed that older adults who had a non-stable weight trajectory (either weight gain, weight loss, or weight cycle) had a higher mortality rate compared to those with stable weight. 1 Phenome-wide association studies (PheWAS), which have the capacity to examine associations of an exposure of interest across the full disease phenome, allow us to test associations between weight trajectory and health status in large-scale electronic health records (EHR), which contain longitudinal data for a large patient population, including frequent objective weight measures, taken as part of typical clinic visits. To date, weight-related PheWAS have used body mass index (BMI)-related genetic scores [2][3][4] /variants 5 or average BMI values 6 as the exposure of interest, but have not yet explored the phenome-wide consequences of longitudinal weight trajectories.…”

Section: Introductionmentioning

confidence: 99%

Exploring the clinical consequences and genetic aetiology of adult weight trajectories

Johnson

Birgegård

et al. 2021

Preprint

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Background: Longitudinal weight trajectories may reflect individual health status. We examined the genetic aetiology and clinical consequences of adult weight trajectories in males and females leveraging genetic and phenotypic data in the electronic health records (EHR) of the BioMeTM Biobank. Methods: We constructed four longitudinal weight trajectories using annual EHR-recorded weights (stable weight, weight gain, weight loss, or weight cycle) (n=21,487). After validating the accuracy of the trajectories (n=100), we conducted a hypothesis-free phenome-wide association study (PheWAS), including sex-stratified PheWAS, to identify diseases associated with each weight trajectory. We then performed a hypothesis-driven polygenic risk score (PRS) analysis on these weight trajectories, focusing on anorexia nervosa (AN) and depression—both commonly associated with weight changes. Findings: Weight trajectory classification was highly accurate (accuracy, sensitivity, and specificity > 97% for all four trajectories). Hypothesis-free PheWAS analyses identified a significant association between depression and weight cycle (OR=1.4, p≤7.7×10-16) after Bonferroni correction, but not with weight gain or loss. Compared to other weight trajectories, we also observed a significant association of osteoporosis-related phecodes with weight loss in females only (ORfemale=1.4, pfemale≤1.4×10-7, ORmale=0.8, pmale≥0.18). AN-PRS was positively associated with weight loss trajectory among individuals without eating disorder diagnoses (ORtop vs. bottom 10% PRS=1.95, p=0.00035). Consistent effect direction was observed across three ancestry groups. The AN-PRS-weight loss association was not attenuated by obesity-PRS (ORtop vs. bottom 10% PRS=1.94). Interpretation: Adult weight trajectory is associated with disease both phenotypically and genetically. Our PheWAS reveals unique relationships between diseases and weight trajectory patterns, including the association of depression and weight cycle trajectory in both males and females, and osteoporosis-weight loss trajectory association in females only. In addition, our PRS analysis suggests that adults with higher AN genetic risk are more likely to have a weight loss trajectory, and this association may be independent of BMI/obesity-related genetic pathways.

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Trends in Self-Reported Adherence to Healthy Lifestyle Behaviors Among US Adults, 1999 to March 2020

Xia

Geng

et al. 2023

JAMA Netw Open

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ImportanceAdherence to a healthy lifestyle is associated with lower risks of adverse outcomes. However, trends in multiple lifestyle factors and overall healthy lifestyle status among US adults in recent years are unknown.ObjectiveTo examine trends in multiple lifestyle factors and overall healthy lifestyle among US adults.Design, Setting, and ParticipantsThis serial cross-sectional study used nationally representative data from 10 National Health and Nutrition Examination Survey (NHANES) cycles (nine 2-year cycles from 1999 to 2016 and 1 combined cycle from 2017 to March 2020) among adults 20 years or older. Data were analyzed from December 10, 2021, to January 11, 2023.ExposureSurvey cycle.Main Outcomes and MeasuresFive healthy lifestyle factors: never smoking, moderate or lighter alcohol consumption (for women: ≤7 drinks/wk; for men: ≤14 drinks/wk), healthy diet (Healthy Eating Index–2015 scores ≥60.0), sufficient physical activity (≥150 min/wk of equivalent moderate physical activity), and healthy weight (body mass index [calculated as weight in kilograms divided by height in meters squared] 18.5-24.9).ResultsA total of 47 852 adults were included in this study. The weighted mean [SE] age was 47.3 [0.2] years; 24 539 (weighted proportion, 51.5%) were women. From the 1999-2000 cycle to the 2017 to March 2020 cycle, the estimated prevalence of the 5 lifestyle factors showed divergent trends, with increasing prevalence of never smoking (from 49.4% [95% CI, 46.4%-52.4%] to 57.7% [95% CI, 55.5%-59.9%]; difference, 8.2% [95% CI, 4.5%-12.0%]), healthy diet (from 19.3% [95% CI, 16.0%-22.6%] to 24.5% [95% CI, 21.5%-27.5%]; difference, 5.2% [95% CI, 0.8%-9.7%]), and sufficient physical activity (from 55.7% [95% CI, 51.8%-59.6%] to 69.1% [95% CI, 67.2%-71.1%]; difference, 13.4% [95% CI, 9.0%-17.8%]), while prevalence of healthy weight decreased from 33.1% (95% CI, 30.5%-35.6%) to 24.6% (95% CI, 22.6%-26.7%; difference, −8.4% [95% CI, −11.8% to −5.1%]) (all P &lt; .001 for trend). Meanwhile, there was no significant trend in moderate or lighter alcohol consumption. Overall, the estimated prevalence of at least 4 healthy lifestyle factors increased from 15.7% (95% CI, 12.8%-18.7%) to 20.3% (95% CI, 17.8%-22.7%; difference, 4.5% [95% CI, 0.7%-8.4%]; P &lt; .001 for trend). Disparities in healthy lifestyle were widened by age group, with little improvement among adults 65 years and older (difference, 0.04% [95% CI, −4.28% to 4.35%]). There were persistent disparities in healthy lifestyle by race and ethnicity, educational level, and income level.Conclusions and RelevanceThe findings of this cross-sectional study of NHANES data over a 22-year period suggest diverse change patterns across 5 healthy lifestyle factors and a modest improvement in overall lifestyle existed among US adults, with worsening or persistent disparities in lifestyle.

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The association of weight change and all-cause mortality in older adults: a systematic review and meta-analysis

Cited by 51 publications

References 28 publications

Objectively Assessed Weight Change and All-Cause Mortality among Community-Dwelling Older People

Objectively Assessed Weight Change and All-Cause Mortality among Community-Dwelling Older People

Exploring the clinical consequences and genetic aetiology of adult weight trajectories

Trends in Self-Reported Adherence to Healthy Lifestyle Behaviors Among US Adults, 1999 to March 2020

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