No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
BMI and all-cause mortality in older adults: A meta-analysis
Abstract
Whether the association between body mass index (BMI) and all-cause mortality for older adults is the same as for younger adults is unclear.
The objective was to determine the association between BMI and all-cause mortality risk in adults ≥65 y of age.
A 2-stage random-effects meta-analysis was performed of studies published from 1990 to 2013 that reported the RRs of all-cause mortality for community-based adults aged ≥65 y.
Thirty-two studies met the inclusion criteria; these studies included 197,940 individuals with an average follow-up of 12 y. With the use of a BMI (in kg/m(2)) of 23.0-23.9 as the reference, there was a 12% greater risk of mortality for a BMI range of 21.0-21.9 and a 19% greater risk for a range of 20.0-20.9 [BMI of 21.0-21.9; HR (95% CI): 1.12 (1.10, 1.13); BMI of 20.0-20.9; HR (95% CI): 1.19 (1.17, 1.22)]. Mortality risk began to increase for BMI >33.0 [BMI of 33.0-33.9; HR (95% CI): 1.08 (1.00, 1.15)]. Self-reported anthropometric measurements, adjustment for intermediary factors, and exclusion of early deaths or preexisting disease did not markedly alter the associations, although there was a slight attenuation of the association in never-smokers.
For older populations, being overweight was not found to be associated with an increased risk of mortality; however, there was an increased risk for those at the lower end of the recommended BMI range for adults. Because the risk of mortality increased in older people with a BMI <23.0, it would seem appropriate to monitor weight status in this group to address any modifiable causes of weight loss promptly with due consideration of individual comorbidities.
... Furthermore, in the most comprehensive pooled analysis of U.S. data to date, the Global BMI Mortality Collaboration conducted a participant-level meta-analysis of data from 45 cohort studies from the U.S. [5]. Nevertheless, epidemiologic evidence regarding the association between BMI and all-cause mortality has been inconsistent, especially with regards to overweight and class I obese individuals, with some meta-analyses demonstrating similar or lower risk of all-cause mortality [6,7] and others finding significantly elevated mortality risk in individuals with BMI >25 [5,8,9]. In addition, most U.S. studies to date have used data from the 1960s through the 1990s and have included predominantly non-Hispanic White men and women. ...
... In contrast, a recent 2014 meta-analysis by Winter et al. showed that among adults aged �65 worldwide, BMI through 30.0 was associated with 4-9% decreased mortality risk compared to a reference of 23.0-23.9 [7]. Although our findings were in the same direction, they were not statistically significant (as were those of Winter et al.), likely because we performed rigorous adjustments and included a broader reference of 22.5-24.9, ...
... Our study included >100,000 minority adults, who exhibited lower mortality risk at overweight and obese BMIs compared to non-Hispanic White adults. This finding is consistent with data from Calle et al. [7] and with subgroup analyses from other large U.S. cohort studies [36,37]. ...
Introduction:
Much of the data on BMI-mortality associations stem from 20th century U.S. cohorts. The purpose of this study was to determine the association between BMI and mortality in a contemporary, nationally representative, 21st century, U.S. adult population.
Methods:
This was a retrospective cohort study of U.S. adults from the 1999-2018 National Health Interview Study (NHIS), linked to the National Death Index (NDI) through December 31st, 2019. BMI was calculated using self-reported height & weight and categorized into 9 groups. We estimated risk of all-cause mortality using multivariable Cox proportional hazards regression, adjusting for covariates, accounting for the survey design, and performing subgroup analyses to reduce analytic bias.
Results:
The study sample included 554,332 adults (mean age 46 years [SD 15], 50% female, 69% non-Hispanic White). Over a median follow-up of 9 years (IQR 5-14) and maximum follow-up of 20 years, there were 75,807 deaths. The risk of all-cause mortality was similar across a wide range of BMI categories: compared to BMI of 22.5-24.9 kg/m2, the adjusted HR was 0.95 [95% CI 0.92, 0.98] for BMI of 25.0-27.4 and 0.93 [0.90, 0.96] for BMI of 27.5-29.9. These results persisted after restriction to healthy never-smokers and exclusion of subjects who died within the first two years of follow-up. A 21-108% increased mortality risk was seen for BMI ≥30. Older adults showed no significant increase in mortality between BMI of 22.5 and 34.9, while in younger adults this lack of increase was limited to the BMI range of 22.5 to 27.4.
Conclusion:
The risk of all-cause mortality was elevated by 21-108% among participants with BMI ≥30. BMI may not necessarily increase mortality independently of other risk factors in adults, especially older adults, with overweight BMI. Further studies incorporating weight history, body composition, and morbidity outcomes are needed to fully characterize BMI-mortality associations.
... Transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement (SAVR) are standard treatments for severe aortic stenosis, especially for patients older than 65 years [1,2]. According to the Society of Thoracic Surgeons (STS) scores, patients are divided into high, intermediate, and low-risk groups. ...
... According to the Society of Thoracic Surgeons (STS) scores, patients are divided into high, intermediate, and low-risk groups. TAVI is the recommended treatment for high-risk patients [1][2][3]. However, for intermediate and low-risk patients, TAVI or SAVR are standard treatments [4,5] because these treatments have different benefits and disadvantages depending on a patient's individual condition, which is evaluated by a heart team. ...
... Moreover, one of the differences between elderly populations in Taiwan and Western countries is the proportion of people with normal and overweight BMI [12,13]. Previous studies revealed that overweight patients have better results than patients with BMI less than 20 [1,2]. The PARTNER trials are three of the largest randomized control trials comparing results between TAVI and SAVR. ...
Background:
The aim of our study was to provide real-world data on outcomes for elderly Taiwanese patients who underwent transcatheter aortic valve replacement or surgical aortic valve replacement in different risk groups.
Methods:
From March 2011 through December 2021, 177 patients with severe aortic stenosis who were ≥70 years old and had undergone TAVI (transcatheter aortic valve implantation) or SAVR (surgical aortic valve replacement) in a single center were divided by STS score (<4%, 4-8% and >8%) into three different groups. Then, we compared their clinical characteristics, operative complications, and all-cause mortality.
Results:
In all risk groups, there were no significant differences in in-hospital mortality, or 1-year and 5-year mortality between patients in the TAVI and SAVR groups. In all risk groups, patients in the TAVI group had shorter hospital stay and higher rate of paravalvular leakage than the SAVR group. After univariate analysis, BMI (body mass index) < 20 was a risk factor for higher 1-year and 5-year mortality. In the multivariate analysis, acute kidney injury was an independent factor for predicting worse outcomes in terms of 1-year and 5-year mortality.
Conclusions:
Taiwan elderly patients in all risk groups did not have significant differences in mortality rates between the TAVI and the SAVR group. However, the TAVI group had shorter hospital stay and higher rate of paravalvular leakage in all risk groups.
... The low risk for BMI and mortality was 24.0-30.9 kg/m 2 , and the lowest risk between 27.0 and 27.9 kg/m 2 (Winter et al., 2014). Despite the association among obesity and morbidity (Goya Wannamethee et al., 2004;Khan et al., 2022), mortality (Winter et al., 2014), and other functional problems (Goya Wannamethee et al., 2004;Vincent et al., 2010;Schaap et al., 2013), the reliability of the BMI for accurately classifying obesity seems not to be valid in adults (Romero-Corral et al., 2008), older adults (Batsis et al., 2016), postmenopausal women (Banack et al., 2018) and athletes (Provencher et al., 2018). ...
... kg/m 2 , and the lowest risk between 27.0 and 27.9 kg/m 2 (Winter et al., 2014). Despite the association among obesity and morbidity (Goya Wannamethee et al., 2004;Khan et al., 2022), mortality (Winter et al., 2014), and other functional problems (Goya Wannamethee et al., 2004;Vincent et al., 2010;Schaap et al., 2013), the reliability of the BMI for accurately classifying obesity seems not to be valid in adults (Romero-Corral et al., 2008), older adults (Batsis et al., 2016), postmenopausal women (Banack et al., 2018) and athletes (Provencher et al., 2018). ...
Background:
The body mass index (BMI) ≥30 kg/m2 is the universally accepted cut-off point for defining obesity; however, its accuracy in classifying obesity in older adults is poorly understood.
Objectives:
To assess the performance of the BMI cut-off point ≥30 kg/m2 in classifying obesity in older adults, using the fat mass index (FMI) and fat mass percentage (FM%) as reference criteria; and to establish region- and sex-specific BMI-based cut-off points to classify obesity in older adults.
Methods:
The present study is a secondary analysis derived from a cross-sectional project that included a sample of 1463 older adults from ten Latin American and Caribbean countries. Volunteers underwent total body water measurements using the deuterium dilution technique to determine FMI and FM%. Accuracy of the BMI and derived cutoff points was assessed by the area under the receiver operating characteristic curve (AUC).
Results:
The BMI cut-off point ≥30 kg/m2 had low sensitivity for classifying obesity in these older adults compared to the FMI and FM%. The AUC values for the optimal BMI-derived cut-off points showed an acceptable-to-outstanding discriminatory capacity in diagnosing obesity defined by the FMI. There was also a better balance between sensitivity and specificity than with the values obtained by a BMI ≥30 kg/m2 in older subjects in both regions.
Conclusion:
The BMI cut-off point ≥30 kg/m2 had poor sensitivity for accurately diagnosing obesity in older adults from two regions. The region- and sex-specific BMI-derived cut-off points for defining obesity using the FMI are more accurate in classifying obesity in older men and women subjects from both regions.
... Obesity is a well-recognized risk factor for diabetes, and body mass index (BMI) is the most commonly used anthropometric parameter to evaluate obesity. However, the limitation that BMI cannot distinguish between fat and lean muscle mass makes it of very limited help in preventing diabetes (8). In recent years, more and more studies have demonstrated that body composition indicators FM and LBM can reflect more obesity-related clinical and public health information than BMI, and that in-depth analysis of the different contributions of FM and LBM to BMI can help explain the obesity paradox (9)(10)(11); in addition, FM and LBM have significant but distinct effects on human glucose metabolism (12,13). ...
... BMI is currently the most commonly used simple anthropometric obesity parameter and is widely used in epidemiological studies and clinical practice for risk estimation and risk stratification of obesity-related diseases. However, the main limitation of BMI is its inability to differentiate between fat and lean muscle mass (8), and using BMI only as an obesity indicator to assess diabetes risk in the general population may result in incorrect risk estimates and inefficient risk stratification, as the athletes and the general population, as well as men and women, clearly have different diabetes risks with the same BMI. Therefore, we speculated that further exploring the association between body composition, FM and LBM, and diabetes risk on the basis of BMI might compensate for the limitation. ...
Objective:
The relationship between body composition fat mass (FM) and lean body mass (LBM) and diabetes risk is currently debated, and the purpose of this study was to examine the association of predicted FM and LBM with diabetes in both sexes.
Methods:
The current study was a secondary analysis of data from the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) cohort study of 15,463 baseline normoglycemic participants. Predicted LBM and FM were calculated for each participant using anthropometric prediction equations developed and validated for different sexes based on the National Health and Nutrition Examination Survey (NHANES) database, and the outcome of interest was diabetes (types not distinguished) onset. Multivariate Cox regression analyses were applied to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of predicted FM and LBM with diabetes risk and further visualized their associations using a restricted cubic spline function.
Results:
The incidence density of diabetes was 3.93/1000 person-years over a mean observation period of 6.13 years. In women, predicted LBM and FM were linearly associated with diabetes risk, with each kilogram increase in predicted LBM reducing the diabetes risk by 65% (HR 0.35, 95%CI 0.17, 0.71; P < 0.05), whereas each kilogram increase in predicted FM increased the diabetes risk by 84% (HR 1.84, 95%CI 1.26, 2.69; P < 0.05). In contrast, predicted LBM and FM were non-linearly associated with diabetes risk in men (all P for non-linearity < 0.05), with an L-shaped association between predicted LBM and diabetes risk and a saturation point that minimized the risk of diabetes was 45.4 kg, while predicted FM was associated with diabetes risk in a U-shape pattern and a threshold point with the lowest predicted FM-related diabetes risk was 13.76 kg.
Conclusion:
In this Asian population cohort, we found that high LBM and low FM were associated with lower diabetes risk according to anthropometric equations. Based on the results of the non-linear analysis, we believed that it may be appropriate for Asian men to keep their LBM above 45.4 kg and their FM around 13.76 kg.
... A recent meta-analysis including 30 studies examining the association between weight change and all-cause mortality in adults ≥ 65 years reported that both weight loss and weight gain in older age were associated with elevated mortality risk, however the mortality risk associated with weight gain was modest (10% increase in mortality risk) (13). There is also some evidence that overweight and obesity in later life might not confer the same mortality risks as seen in middle age (14). This study aimed to examine the association between self-reported weight in early (at 18 years of age) and later adulthood (at 70 years and over), collected at age ≥ 70 years, and change in weight status over time between these ages, with the risk of mortality from all causes. ...
... The results of our study suggest that development of high BMI in older age is not a strong risk factor for mortality, however it is acknowledged that we could not determine for how long a healthy BMI was maintained in earlier adult life. Our findings align with meta-analyses on the BMI-mortality relationship (3,14), which reported that obesity (BMI of ≥30) was not significantly associated with all-cause mortality in older adults compared to a normal BMI. Our findings add to evidence that the effect of obesity on mortality risk tends to decline with increasing age (28) and obesity in early adulthood was a greater contributor to increased mortality risk. ...
Objectives
The extent to which body weight in early adulthood is associated with late-life mortality risk is unclear. This study aimed to determine the association between body mass index (BMI) in early adulthood (at 18 years of age) and older age (70 years and over), and the risk of mortality in later life.
Design
Secondary analysis of the ASPREE Longitudinal Study of Older Persons (ALSOP).
Setting, Participants
Data were from 14,853 relatively healthy community-dwelling Australians aged ≥70 years when enrolled in the study.
Measurements
Self-reported weight atage ≥70 years and recalled weight at age 18 years were collected at ALSOP study baseline. Height was measured with a stadiometer and was used for calculation of BMI at both timepoints. BMI at each timepoint was defined as: underweight, normal weight, overweight and obese. Individuals were categorised into one of five ‘lifetime’ BMI groups: normal weight (BMI between 18.5 and 24.9 at both times), overweight (25.0–29.9 at either or both times), obesity to non-obese (≥30.0 at age 18 and <30.0≥70 years), non-obese to obesity (<30.0 at age 18 and ≥30.0 at age≥70 years), and early and later life obesity (≥30.0 at both times).
Results
During a median 4.7 years follow-up, 715 deaths occurred. Obesity at 18 years, but not in older age (p=0.44), was significantly associated with the risk of mortality in later life, even after accounting for current health status (HR: 2.35, 95% CI: 1.53–3.58, p<0.001). Compared with participants with normal BMI at both time points, being obese at both time points was associated with increased mortality risk (HR=1.99, 95% CI: 1.04–3.81, p=0.03), and the risk was even greater for individuals who were obese at 18 years but were no longer obese in older age (HR=2.92, 95% CI: 1.65–5.16, p<0.001), in fully adjusted models. Participants who were normal weight at 18 years and were obese in later life, did not have an increased mortality risk (p=0.78).
Conclusions
Obesity in early adulthood, and obesity in both early and later life, were associated with increased mortality risk in later life. This highlights the importance of preventing obesity in early adulthood and maintaining a normal weight over an adult lifespan.
... On the other hand, although children, adolescents, adults, and older adults may share some factors that contribute to overweight, the effects of overweight and obesity in old age are still uncertain [30]. For example, a meta-analysis that assessed mortality risks in older adults (aged 65 years or older) demonstrated that the increased risk of mortality occurred for individuals with a BMI <23 kg/m 2 [31], while being overweight was not associated with an increased risk of mortality for older populations [32]. Contradicting these results, another study which included nearly 900,000 adults by the Prospective Studies Collaboration found a 30% increase in mortality risk for every 5-unit increase in BMI above 22.5-25 kg/m 2 [33]. ...
... Additionally, using specific BMI cut-off points helped avoid overestimation of the prevalence of overweight and obesity. As some authors have noted, the WHO healthy weight range for adults may not be appropriate for older adults [31]. ...
Despite extensive research on overweight and obesity, there are few studies that present longitudinal statistical analyses among non-institutionalized older adults, particularly in low- and middle-income countries. This study aimed to assess the prevalence and factors associated with excess weight in older adults from the same cohort over a period of fifteen years. A total of 264 subjects aged (≥60 years) from the SABE survey (Health, Wellbeing and Aging) in the years 2000, 2006, 2010, and 2015 in the city of São Paulo, Brazil, were evaluated. Overweight was assessed by a BMI of ≥28 kg/m2. Multinomial logistic regression models adjusted for sociodemographic and health data were used to assess factors associated with excess weight. After normal weight, overweight was the most prevalent nutritional status in all evaluated periods: 34.02% in 2000 (95%CI: 28.29–40.26); 34.86% in 2006 (95%CI: 28.77–41.49%); 41.38% in 2010 (95%CI: 35.25–47.79); 33.75% in 2015 (95%CI: 28.02–40.01). Being male was negatively associated with being overweight in all years (OR: 0.34 in 2000; OR: 0.36 in 2006; OR: 0.27 in 2010; and OR: 0.43 in 2015). A greater number of chronic diseases and worse functionality were the main factors associated with overweight, regardless of gender, age, marital status, education, physical activity, and alcohol or tobacco consumption. Older adults with overweight and obesity, a greater number of chronic diseases, and difficulties in carrying out daily tasks required a greater commitment to healthcare. Health services must be prepared to accommodate this rapidly growing population in low- and middle-income countries.
... This measure may be influenced by the physiological state, fluid retention, limb loss, and food intake of individuals, or may vary as an adverse effect of certain medications [2]. Moreover, it is strongly associated with mortality outcomes for older adult residents of long-term care institutions [3][4][5]. ...
... Malnutrition, with consequent weight loss, is one of the factors most often associated with mortality among older adults [3][4][5][6]. Thus, offering adequate health care to the residents of Long-term Care Institutions for older adults (LTCIs) is important to monitor their nutritional status. ...
Background:
Weight measurement is important in the nutritional anthropometric monitoring of older adults. When this measurement is not possible, estimates may be used.
Aim:
Developing and validating weight predictive equations for older adult residents in long-term care institutions in Brazil.
Subjects and methods:
The sample comprised 393 older adult residents in long-term care institutions. Data were collected in two stages, with 315 older adults in the first and 78 in the second. We have measured the arm, calf, and waist circumferences, as well as the triceps and subscapular skinfold and knee height. Multiple linear regression was used to develop the equations, which were evaluated through the coefficient of determination, standard error of estimation, Akaike information criterion, intraclass correlation coefficient (ICC), and Bland-Altmann plot.
Results:
Five models with different anthropometric measurements were developed, (1) arm circumference as a discriminant variable (ICC: 0.842); (2) best statistical fit for men and women (ICC: 0.874) and its stratification by sex (3) (ICC: 0.876); (4) easy-to-perform measurement for men and women (ICC: 0.842) and its stratification by sex (5) (ICC: 0.828).
Conclusion:
Five models for estimating the weight of older adult residents in long-term care institutions were developed and validated. The choice to use the models should be based on the physical capacity of the older adults to be evaluated.
... BMI < 23 or ≥ 31 was classified as unhealthy based on guidelines for adults aged 65 and over [29]. Physical activity was assessed using the International Physical Activity Questionnaire-Short Form (IPAQ-SF) [30]. ...
Background
In this study, we examined the effect of alcohol, as well as the combined effect of seven lifestyle factors, on all-cause mortality in older adults (baseline age 70 years).
Methods
Data was derived from the population-based Gothenburg H70 Birth Cohort study, including 1124 participants from the 2014–16 examination. Risk consumption was defined as > 98 g alcohol per week, and hazardous drinking was based on the Alcohol Use Disorders Identification Test-Consumption questionnaire (AUDIT-C). Cox regression models were used to examine the individual effect of alcohol consumption, as well as the combined effect of seven lifestyle risk factors (high alcohol consumption, lifetime smoking, unhealthy Body Mass Index, insufficient physical activity, sedentary behavior, insufficient/prolonged sleep, unhealthy dietary pattern) on all-cause mortality.
Results
During a mean follow-up of 7.7 years, 81 (7.2%) participants died. Neither risk consumption nor hazardous drinking were associated with elevated mortality, but hazardous drinking was associated with an increased risk of mortality in those with insufficient physical activity. Those with at least five lifestyle risk factors had an increased all-cause mortality compared to those fulfilling criteria for a maximum of one lifestyle risk factor. High alcohol consumption showed a relatively minor impact on this risk, while physical activity and unhealthy dietary pattern had an independent effect on mortality.
Conclusions
In this particular sample, there was no independent effect of alcohol on the risk of 8-year all-cause mortality. However, an interaction effect of physical activity was observed. It may be that high alcohol consumption per se is less important for mortality among older adults. However, a combination of several unhealthy lifestyle behaviors was linked to a substantial increase in the risk of mortality in Swedish older adults. Also, it has to be emphasized that high alcohol consumption may have other adverse health effects apart from mortality among older adults.
... In the general population, for example, it was shown that the association between BMI and all-cause-mortality tends to differ by age [20]. In fact, several studies observed that overweight, when compared with normal weight, was a protective factor for all-cause mortality in older adults [21][22][23]. In patients with EBC, age-dependent associations between BMI and death from any cause have also been reported (Lammers S.W.M., Geurts S.M.E., van Hellemond I.E.G. et al. ...
Purpose
This study determines the prognostic impact of body mass index (BMI) in patients with hormone receptor-positive/human epidermal growth factor receptor-2-negative (HR+/HER2-) advanced (i.e. metastatic) breast cancer (ABC).
Methods
All patients diagnosed with HR+/HER2- ABC who received endocrine therapy with or without a cyclin-dependent kinase (CDK) 4/6 inhibitor as first-given systemic therapy between 2007 and 2020 in the Netherlands were identified from the Southeast Netherlands Advanced Breast Cancer (SONABRE) registry (NCT03577197). Patients with a recorded BMI were categorised as underweight (<18.5 kg/m²), normal weight (18.5-24.9 kg/m²), overweight (25-29.9 kg/m²), or obese (≥30 kg/m²). Overall survival (OS) and progression-free survival (PFS) were compared between BMI classes using multivariable Cox regression analyses.
Results
This study included 1,456 patients, of whom 35 were underweight, 580 normal weight, 479 overweight, and 362 obese. No differences in OS were observed between normal weight patients and respectively overweight (HR=0.99; 95% CI: 0.85-1.16) and obese patients (HR=1.04; 95% CI: 0.88-1.24). However, the OS of underweight patients (HR=1.45; 95% CI: 0.97-2.15) tended to be worse than the OS of normal weight patients. When compared with normal weight patients, the PFS was similar in underweight (HR=1.05; 95% CI: 0.73-1.51), overweight (HR=0.90; 95% CI: 0.79-1.03), and obese patients (HR=0.88; 95% CI: 0.76-1.02).
Conclusion In this study among 1,456 patients with HR+/HER2- ABC, overweight and obesity were prevalent, whereas underweight was uncommon. When compared with normal weight, overweight and obesity were not associated with either OS or PFS. However, underweight seemed to be an adverse prognostic factor for OS.
... The concept of the "obesity paradox" may apply specifically to older population; therefore, the impact of obesity on cardiovascular disease and mortality remain unclear (9,10). A meta-analysis including approximately 200 000 individuals aged ≥ 65 years showed that being overweight was not associated with an increased risk of mortality and that BMIs lower than 23 kg/m 2 and higher than 33 kg/m 2 were associated with an increased risk of mortality, suggesting a U-shaped curve of BMI for mortality (11). A potential explanation for the "obesity paradox" is the approximate estimation of body fat by BMI;, body composition is not assessed by this index, including the distinction between lean and fat body mass, which opposite impacts on the risk of mortality. ...
Sarcopenic obesity is defined as the coexistence of sarcopenia and obesity in the same individual, characterized by of the co-presence of body fat accumulation and muscle loss. This condition is currently a major concern as it is associated with frailty and disabilities such as cardiovascular disease, fractures, dementia, cancer, and increased all-cause mortality. Particularly, older individuals remain at risk of sarcopenic obesity. Progress at several levels is needed to improve the global prognostic outlook for this condition, including the elaboration and implementation of a more uniform definition that may favor the identification and specification of prevalence by age group. Furthermore, improvements in the understanding of the pathogenesis of sarcopenic obesity may lead to the development of more specific therapeutic interventions to improve prognosis. We reviewed the knowledge on sarcopenic obesity and its associations with cardiovascular diseases and mortality.
... Similarly, our previous analysis in this cohort found that overweight and healthy-weight participants had a similar risk of all-cause mortality [16]. In fact, being overweight in late adulthood may be protective, as meta-analyses on the BMI-mortality relationship among older adults reported reduced risks of all-cause mortality for overweight status participants compared to those with a healthy weight [6,28]. Hence, the BMI-mortality relationship is often described as U-shaped. ...
Objective:
To identify the socio-demographic, lifestyle, and clinical characteristics associated with self-reported weight status in early (age 18 years) and late (age ≥ 70 years) adulthood.
Methods:
The number of participants was 11,288, who were relatively healthy community-dwelling Australian adults aged ≥70 years (mean age 75.1 ± 4.2 years) in the Aspirin in Reducing Events in the Elderly (ASPREE) Longitudinal Study of Older Persons (ALSOP) sub-study. Self-reported weight at the study baseline (age ≥ 70 years) and recalled weight at age 18 years were collected. Height measured at baseline was used to calculate the BMI at both time points. Individuals were categorised into one of five 'lifetime' weight status groups: healthy weight (at both age 18 year and ≥70 years), overweight (at either or both times), non-obese (age 18 year) to obesity (age ≥70 years), obesity (age 18 years) to non-obese (age ≥ 70 years), and early and later life obesity (at age 18 years and ≥70 years).
Results:
Participants who experienced obesity in early and/or late adulthood were at a higher risk of adverse clinical characteristics. Obesity in late adulthood (regardless of early adulthood weight status) was associated with high proportions of hypertension, diabetes, and dyslipidaemia, whereas obesity in early adulthood (regardless of late adulthood weight status) was associated with lower cognitive scores (on all four measures).
Discussion/conclusion:
Healthy or overweight weight status in early and later adulthood was associated with more favourable socioeconomic, lifestyle, and clinical measures. Obesity in early adulthood was associated with lower cognitive function in later adulthood, whereas obesity in later adulthood was associated with hypertension, diabetes, and dyslipidaemia.
... kg/m 2 , obese = 30.0 kg/ m 2 or greater for persons both younger and older than 80 (Villareal et al., 2011;Flegal et al., 2013;Waters et al., 2013;Winter et al., 2014;Porter Starr and Bales, 2015). Very few of the surveyed seniors had a BMI below 23 kg/m 2 , and general adult population standards were applied to obtain a successfully representative sample. ...
Background: Little is known about changes in anthropometric and body composition (A&BC) characteristics during the aging process. Research indicates that body composition can be linked with socioeconomic status (SES), health status (HS), and physical activity (PA) levels.
Aim: The aim of this study was to evaluate age-related changes in A&BC characteristics in female seniors aged 60+ in view of their SES, HS, and PA levels.
Methods: The survey was conducted in November and December 2022 on a total of 661 female seniors. A questionnaire survey was conducted to obtain information about the participants’ socioeconomic status (chronic diseases, health status, marital status, membership in social organizations, financial status, place of residence, education). The respondents’ PA levels were assessed with the International Physical Activity Questionnaire (IPAQ), and their A&BC characteristics were determined in a bioelectrical impedance analysis with the InBody 270 body composition analyzer. The relationships between A&BC characteristics and age were evaluated based on the values of the Pearson correlation coefficient ( r ).
Results: The mean values of Percent Body Fat (PBF), Body Mass Index (BMI), and the waist-hip ratio (WHR) were relatively high (37.2%, 28.5 kg/m ² , and 0.8, respectively) and indicative of overweight and gynoid obesity. A higher number of significant negative correlations between A&BC characteristics and age were observed in seniors with lower values of SES, HS, and PA, which points to more rapid involutional changes in this group of respondents. A segmental analysis also revealed significantly lower values of fat-free mass (FFM) and body fat mass (BFM) (both indicators were calculated in percentage and kg), in particular in the upper limbs, in women with lower SES, HS, and PA levels.
Conclusion: Environmental factors, including biological, physiological, environmental, psychological, behavioral, and social factors, are significantly associated with aging in women. Age-related changes in A&BC characteristics tend to proceed more rapidly in female seniors with low values of SES and HE and insufficient PA levels.
... Body mass index (BMI) is more typically used to reflect an individual's overall obesity condition, even in the absence of abdominal obesity, and BMI has been demonstrated to be associated with CVRF and CVD [34]. However, an increasing number of studies have found a significant reduction in CVD risk when individuals were metabolically healthy and obese [35,36]; additionally, overweight/obesity as assessed by BMI has been found to be contradictory to mortality in the advanced age group, i.e., the "obesity paradox" [37,38]. Thus, the association direction between BMI in traditional and non-traditional CVRF might be altered in the elderly population. ...
Background
Metabolic syndrome (MetS), a clustering of traditional cardiovascular risk factors (CVRF), is currently one of the major global public health burdens. However, associations between MetS and non-traditional CVRF represented by uric acid (UA), homocysteine (HCY) and hypersensitive C-reactive protein (HsCRP) have not been well explored in the elderly population, especially when considering body mass index (BMI).
Methods
Participants from the Shanghai Elderly Cardiovascular Health (SHECH) study cohort in 2017 were analyzed. MetS was defined using the modified American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Logistic regression models were used to assess associations of non-traditional CVRF, BMI with MetS.
Results
Of the 4360 participants analyzed, 2378 (54.5%) had MetS, the mean (SD) UA was 331 (86) µmol/L, and the median (IQR) HCY and HsCRP were 15 (13–18) µmol/L and 1.0 (0.5–2.1) mg/L, respectively. Participants with higher non-traditional CVRF tended to have a higher significant risk of MetS (P < 0.001), which did not changed substantially in most population subgroups (P-interaction > 0.05). BMI mediated 43.89% (95%CI: 30.38–57.40%), 37.34% (95% CI: 13.86–60.83%) and 30.99% (95%CI: 13.16–48.83%) of associations of hyperuricemia (HUA), hyperhomocysteinemia (HHCY) and high HsCRP (HHsCRP) with MetS, respectively. Abnormal non-traditional CVRF combined with overweight/obesity greatly increased MetS risk (adjusted OR(95%CI): HUA + Overweight: 5.860(4.059-8.461); 6.148(3.707–10.194); HHCY + Overweight: 3.989(3.107-5.121); HHCY + Obese: 5.746(4.064–8.123); HHsCRP + Overweight: 4.026(2.906-5.580); HHsCRP + Obese: 7.717(4.508–13.210)).
Conclusions
In the Chinese elderly population, HUA, HHCY, and HHsCRP were all significantly and independently associated with MetS, supporting the potential of focusing on non-traditional CVRF interventions for preventing and controlling MetS. BMI played moderate mediating roles in associations between non-traditional CVRF and MetS, and abnormal non-traditional CVRF combined with overweight/obesity had significant synergistic effects on MetS risk, highlighting the importance of better weight management in the elderly population.
... However, although this is consistent with much thinking, this has not been found in all studies. 48,50 Furthermore, looking at total body fatness itself may be less helpful than anatomic distribution of body fat. Distinct types and anatomic depots of body fat may have different associations with health outcomes. ...
... Obesity, an important risk factor for chronic diseases, is associated with premature mortality in adults (1); however, this association has been inconsistent in the older population (2,3). Several studies reported a decrease in the mortality rate in older adults who were overweight or obese compared to those with normal weight (4,5), and this protective effect of excess weight on survival is called the "obesity paradox" (6). ...
Background:
Obesity is associated with premature mortality in adults; however, this association has been inconsistent in the older adult population. In addition, there is a lack of specific cutoff points for indicators of negative health outcomes in older adults. Methods: This is a prospective study with 796 non-institutionalized older adults. Data on sociodemographic characteristics, lifestyle, food consumption, and nutritional status were obtained at baseline. Generalized additive models were used to identify cutoff points for the waist circumference (WC) and waist-to-height ratio (WHtR) and Cox proportional hazards models to assess the independent association between adiposity and mortality.
Results:
Over the 9 years of follow-up, 197 deaths (24.7%) occurred, of which 51.8% were men, with a mean age of 76.1 ± 9.0 years. Older adults at higher risk of death had WHtR of <0.52 or ≥0.63 and WC of <83 cm or ≥101 cm. An increased risk of death was observed in older adults with high WC (HR: 2.03 95% CI: 1.20-3.41) and high WHtR (HR: 1.51 95% CI: 1.01-2.26) in the adjusted models, and an increase in WC was a risk factor for higher CVD mortality (HR: 2.09, 95% CI: 1.12-3.88) in the adjusted models.
Conclusion:
Adiposity was associated with an increased risk of death in older adults. In view of these results and considering the lack of cutoff points for anthropometric indices in Brazilian older adults, further studies are needed to confirm the WC and WHtR cutoff values found in this study.
... Self-reported body mass and height were used to calculate the body mass index (BMI) by dividing their body mass by the square of their height in metres. The subjects were grouped using the BMI cut-off points of <18.5, <25.0, and ≥25.0 kg/m 2 for underweight, normal weight, and overweight, respectively [34]. Physical activity data were used for the calculation of daily energy expenditure for physical activity (DEEPA) [35]. ...
Due to their specific mode of operation, military personnel are challenged physically as well as mentally. In most countries, the use of food supplements by military personnel is not regulated, and a high prevalence of supplementation is expected. However, data on this are scarce or very limited, without insights into the importance of supplementation for the intake of bioactive substances. Our goal was, therefore, to develop a study protocol to enable an assessment of the prevalence of using food supplements and an estimate of the contribution of supplementation practices to the dietary intake of specific nutrients and other compounds. The protocol was tested in a study of Slovene Armed Forces (SAF) personnel. Data were collected using an anonymous questionnaire in a sample of 470 participants from different military units—about half from the barracks located across the country, and the other half returning from military operations abroad. To provide meaningful results, we recorded the use of food supplements and functional foods available in single-sized portions (i.e., energy drinks, protein bars, etc.). Altogether, 68% of the participants reported supplementation, most commonly with vitamin, mineral, and protein supplements. Military rank, participation status in military operations, and physical activity were the main determinants of the specific supplements used. Surprisingly, a lower prevalence of overall and protein supplementation was observed in subjects returning from military operations abroad (62 vs. 74%) than in personnel stationed in barracks across Slovenia; however, the frequency of the use of energy drinks and caffeine supplements was higher in this population (25 vs. 11%). The study design allowed for estimations of the daily intake of supplemented bioactive compounds. We describe the challenges and approaches used in the study to support similar studies in the future and within other populations.
... This paradoxical relationship has been shown in various cohort studies and meta-analyses for those aged over 65 years old. They reported less (29)(30)(31) or similar (32) mortality risk for overweight or obese individuals compared to normal weight in older persons. There is still a need to explore the effect of central adiposity on MM and then mortality development in older people. ...
The coexistence of several chronic diseases is very common in older adults, making it crucial to understand multimorbidity (MM) patterns and associated mortality. We aimed to determine the prevalence of MM and common chronic disease combinations, as well as their impact on mortality in men and women aged 65 years and older using the population-based KORA-Age study, based in South of Germany. The chronic disease status of the participants was determined in 2008/9, and mortality status was followed up until 2016. MM was defined as having at least two chronic diseases. We used Cox proportional hazard models to calculate the hazard ratios (HRs) and the 95% confidence intervals (CIs) for associations between MM and all-cause mortality. During the study period 495 men (24.6%) and 368 women (17.4%) died. Although the MM prevalence was almost the same in men (57.7%) and women (60.0%), the overall effect of MM on mortality was higher in men (HR: 1.81, 95% CI: 1.47-2.24) than in women (HR: 1.28, 95% CI: 1.01-1.64; p-value for interaction <0.001). The type of disease included in the MM patterns had a significant impact on mortality risk. For example, when both heart disease and diabetes were included in the combinations of two and three diseases, the mortality risk was highest. The risk of premature death does not only depend on the number of diseases but also on the specific disease combinations. In this study, life expectancy depended strongly on a few diseases, such as diabetes, hypertension, and heart disease.
... De nitions of alcohol consumption and smoking are described above. BMI < 23 or ≥ 31 was classi ed as unhealthy based on guidelines for adults aged 65 and older (29). Physical activity was assessed using the International Physical Activity Questionnaire-Short Form (IPAQ-SF) (30). ...
Background The relationship between alcohol consumption and health is not fully understood. In this study, we examined the effect of alcohol as well as the combined effect of seven lifestyle factors on all-cause mortality in older adults (baseline age 70 years).
Methods Data was derived from the population-based Gothenburg H70 Birth Cohort study, including 1124 participants from the 2014-16 examination. Risk consumption was defined as >98 grams and hazardous drinking based on the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire. Cox regression models were used to examine the individual effect of alcohol consumption, as well as the combined effect of seven lifestyle risk factors (high alcohol consumption, lifetime smoking, unhealthy Body Mass Index, insufficient physical activity, sedentary behavior, insufficient/prolonged sleep, unhealthy dietary pattern) on all-cause mortality.
Results During a mean follow-up of 7.7 years, 81 (7.2%) participants died. Neither risk consumption nor hazardous drinking were associated with elevated mortality, but hazardous drinking was associated with an increased risk of mortality in those with insufficient physical activity. Those with at least five lifestyle risk factors had an increased all-cause mortality compared to those fulfilling criteria for maximum one lifestyle risk factor, and high alcohol consumption contributed least to this risk, while physical activity and unhealthy dietary pattern had an independent effect on mortality.
Conclusions In this population study on 70-year-olds, neither risk consumption nor hazardous drinking were associated with 8-year all-cause mortality. Among seven life styles factors studied, risk or hazardous drinking of alcohol contributed least to mortality. It may be that high alcohol consumption is less important for mortality among older adults, especially in those with higher physical activity. However, it has to be emphasized that high alcohol consumption may have other adverse health effects among older adults.
... n = 14 studies) 8 . Many of the individual studies included in these meta-analyses were small, undertaken in past decades when modern preventive interventions were limited and/or used self-reported measures of body size 1,19 . Moreover, a substantial proportion were initiated at a time when CVD dominated as a cause of death in high income countries 20 . ...
In the general population, body mass index (BMI) and waist circumference are recognized risk factors for several chronic diseases and all-cause mortality. However, whether these associations are the same for older adults is less clear. The association of baseline BMI and waist circumference with all-cause and cause-specific mortality was investigated in 18,209 Australian and US participants (mean age: 75.1 ± 4.5 years) from the ASPirin in Reducing Events in the Elderly (ASPREE) study, followed up for a median of 6.9 years (IQR: 5.7, 8.0). There were substantially different relationships observed in men and women. In men, the lowest risk of all-cause and cardiovascular mortality was observed with a BMI in the range 25.0–29.9 kg/m² [HR25-29.9 vs 21–24.9 kg/m²: 0.85; 95% CI, 0.73–1.00] while the highest risk was in those who were underweight [HRBMI <21 kg/m2 vs BMI 21–24.9 kg/m2: 1.82; 95% CI 1.30–2.55], leading to a clear U-shaped relationship. In women, all-cause mortality was highest in those with the lowest BMI leading to a J-shaped relationship (HRBMI <21 kg/m2 vs BMI 21–24.9 kg/m2: 1.64; 95% CI 1.26–2.14). Waist circumference showed a weaker relationship with all-cause mortality in both men and women. There was little evidence of a relationship between either index of body size and subsequent cancer mortality in men or women, while non-cardiovascular non-cancer mortality was higher in underweight participants. For older men, being overweight was found to be associated with a lower risk of all-cause mortality, while among both men and women, a BMI in the underweight category was associated with a higher risk. Waist circumference alone had little association with all-cause or cause-specific mortality risk.
Trial registration ASPREE https://ClinicalTrials.gov number NCT01038583.
... However, gaining BMI can also have undesirable metabolic risks including excess adiposity accumulation, which leads to cardiovascular diseases and diabetes mellitus (3). Body composition analyses have also reported that excess body fat increases all-cause and disease-cause mortality, and people with low lean mass have been found to have higher death rates (4,5). Therefore, the management of an optimal body composition for old people is important. ...
Background
Elderly people with low lean and high fat mass, are diagnosed with sarcopenic obesity (SO), and often have poor clinical outcomes. This study aimed to explore the relationship between obesity and sarcopenia, and the optimal proportion of fat and muscle for old individuals.
Methods
Participants aged 60 years or above were instructed to perform bioelectrical impedance analysis to obtain the muscle and fat indicators, and handgrip strength was also performed. Sarcopenia was diagnosed according to predicted appendicular skeletal muscle mass and function. Body mass index (BMI) and body fat percentage (BF%) were used to define obesity. The association of muscle and fat indicators were analyzed by Pearson’s correlation coefficient. Pearson Chi-Square test was utilized to estimate odds ratios (OR) and 95% confidence intervals (CI) on the risk of sarcopenia according to obesity status.
Results
1637 old subjects (74.8 ± 7.8 years) participated in this study. Not only fat mass, but also muscle indicators were positively correlated to BMI and body weight (p < 0.05). Absolute muscle and fat mass in different positions had positive associations (p < 0.05). Muscle mass and strength were negatively related to appendicular fat mass percentage (p < 0.05). When defined by BMI (OR = 0.69, 95% CI [0.56, 0.86]; p = 0.001), obesity was a protective factor for sarcopenia, whilst it was a risk factor when using BF% (OR = 1.38, 95% CI [1.13, 1.69]; p = 0.002) as the definition. The risk of sarcopenia reduced with the increase of BMI in both genders. It was increased with raised BF% in males but displayed a U-shaped curve for females. BF% 26.0–34.6% in old females and lower than 23.9% in old males are recommended for sarcopenia and obesity prevention.
Conclusion
Skeletal muscle mass had strong positive relationship with absolute fat mass but negative associations with the percentage of appendicular fat mass. Obesity was a risk factor of sarcopenia when defined by BF% instead of BMI. The management of BF% can accurately help elderly people prevent against both sarcopenia and obesity.
... BMI ranged from 15 to 62 (median of 27.5) for those with complete data. It has been suggested that a recommended normal BMI range for seniors is [23, 31), and 60% of the MESA cohort were in this category (Winter et al., 2014). When modeling with the adjustment variables alone, they explained 15% of the variation in BMI. ...
... Height, body mass, waist circumference, and blood pressure were measured by trained fieldworkers using standard protocols. Body mass index was calculated from the height and body mass and evaluated based on age-adjusted cut points (Winter, MacInnis, Wattanapenpaiboon, & Nowson, 2014). ...
Background: Reports of older adults’ perceptions of ageing and health generally do not consider individual
health status, instead presenting large cohort data or focussing on specific population groups. Moreover,
qualitative studies have largely included participants with suboptimal health.
Aim: This study aimed to examine functionally healthy older adults’ perceptions of health and healthy ageing.
Method: Twenty-two functionally healthy older adults living independently in the community (aged 61–83
years; 68% female) participated in six focus groups to explore their perceptions about “health” and “healthy
ageing”. Quantitative measures were used to describe participants’ health status.
Findings: Seven themes describing participants’ experiences of healthy ageing were identified: “know
thyself”, “knowledge and information management”, “choices, agency, and control”, “autonomy and flexibility”,
“being strategic”, “community connections”, and “getting more out of life”. Key competencies for
healthy ageing were subsequently derived, drawing connections between beliefs, behaviours, and knowledge
about healthy ageing.
Discussion: The identified health behaviours perceived to be important for healthy ageing align with previous
reports. The subsequent overarching healthy-ageing competencies (“recognise opportunity”, “strategise”,
“maximise benefits”, and “active participation”) present important pillars underpinning the process
of healthy ageing that have not been previously considered in this context.
Conclusion: The behaviours perceived to influence older adults’ health are varied, and the competencies
identified in this study present a broad framework underpinning these behaviours. The identified competencies
have the potential to inform public health initiatives, practice, and policy, empowering individuals
to optimise their health.
... Среди лиц старшей возрастной группы ИМТ перестает быть столь однозначным фактором неблагоприятного прогноза. Исследования последних лет показали, что среди пожилых пациентов низкий и нормальный ИМТ (<23) ассоциирован с более высокой смертностью, чем у тех, у кого он был в интервале от 23 до 32 (что соответствует избыточной массе тела и даже частично ожирению 1 степени) [9]. Среди участников исследования дефицит массы тела и ожирение встречались достаточно редко 13,4% и 7,3%, соответственно. ...
Over the past century, an increase in life expectancy has been observed in Russia and in the world. According to the United Nations, by 2100, the number of centenarians worldwide will reach 25 million. Despite the annual increase in the number of super-centenarians, this age group remains poorly understood. Aim . To estimate the prevalence of cardiovascular diseases (CVD) and the main risk factors among super-centenarians in Moscow. Material and methods . According to the register of long-livers in Moscow, 82 people aged 95 to 105 were included. Participants were examined at home.The history of life and the presence of chronic diseases was collected by participant words. To assess the state of cardiovascular system, an ultrasound of the heart and main arteries was performed. Results . Conventional CVD risk factors were the exception rather than the rule among study participants (smoking — 8 patients (9,8%), alcohol abuse — 4 (4,9%), obesity — 6 (7,3%)). Dyslipidemia was relatively widespread (n=37; 45,1%), however, there were no pronounced abnormalities in the lipid profile: the maximum increase in low-density lipoproteins was 5,6 mmol/L. The most common CVDs among the participants were hypertension (n=64; 78%), coronary artery disease (n=42; 51,2%), and heart failure (n=26; 31,7%); other diseases were much less common. The most common echocardiographic changes were left atrial dilatation (n=38; 74,5%), increased left ventricular mass, thickening of left ventricular posterior wall (n=24; 48%) and interventricular septum (n=51; 100%). Diastolic and systolic heart failure were not widespread among long-livers: 16 (32%) and 2 (3,9%), respectively. Despite a rather large number of atherosclerotic plaques in the common carotid and femoral arteries, the number of hemodynamically significant plaques was low (n=3; 4,6%). An intima-media thickening up to 1,0-1,1 mm was found. Conclusion . Long-livers in Moscow are characterized by a low prevalence of traditional CVD risk factors (with the exception of hypertension) and a fairly high prevalence of atherosclerotic CVDs, which are characterized by a subclinical course.
... Somewhat paradoxically those aged 65-75 years in the United Kingdom (UK) are at greater risk of both excess body mass and low body mass, compared with young-and mid-life adults [1]. However, it is low body mass and a loss of mass, rather than excess body mass or gains in mass, that pose the greatest risk of mortality in older adults [2,3]. As such, malnutrition-in this context referring to a lack of intake of nutritional requirements leading to loss of body mass and function-is a major healthcare challenge in older people, contributing to negative health outcomes, including sarcopenia and frailty, and adversely affecting independence and quality of life, to large healthcare cost [4]. ...
Poor appetite in later life—termed “anorexia of ageing”—is acknowledged as a key determinant of age-related malnutrition. While physical activity (PA) is often recommended for increasing drive to eat, these recommendations are not well-evidenced in the older population. In this opinion piece we outline limitations to physical activity recommendations in anorexic older adults. We then discuss current evidence for the relationship between physical activity and appetite amongst younger adults and postulate how this relationship may change in later life, with implications regarding future recommendations and research.
Background
Patient‐reported outcome measures have been shown to have important prognostic value after various cardiac interventions. We assessed the association between the change in Kansas City Cardiomyopathy Questionnaire 12 (KCCQ‐12) score after transcatheter aortic valve replacement and mortality.
Methods and Results
We included patients who underwent transcatheter aortic valve replacement at Mayo Clinic between February 2012 to June 2022 and who completed a KCCQ‐12 before and 30 to 45 days after the procedure. Patients were categorized into 3 groups: those who experienced significant (>+19 points; group 1), modest (1–19 points; group 2), and no (≤0 points; group 3) improvement. A total of 1124 patients were included: 60.8% men; 97.6% White. Mean age was 79.4±8.3 years, baseline KCCQ‐12 score was 53.9±24.5, and median Society of Thoracic Surgeons score was 4.9% (interquartile range, 3.1–8.0). At 45 days, the mean change in KCCQ‐12 score was 19±24 points; 46.3% (n=520) of patients had a significant improvement in their KCCQ‐12 score, while 33.4% (n=375) and 20.4% (n=229) had modest and no improvement, respectively. Median survival was higher in group 1 (5.7±0.2 years) compared with groups 2 and 3 (5.1±0.3 and 4.1±0.4 years, respectively; P <0.001). Compared with patients in group 1, those in groups 2 and 3 had higher long‐term risk‐adjusted mortality (adjusted hazard ratios, 1.54 [95% CI, 1.20–1.96], and 2.30 [95% CI, 1.74–3.04], respectively).
Conclusions
Patients who experience modest or no improvement in KCCQ‐12 score after transcatheter aortic valve replacement have substantially higher long‐term mortality. Delta KCCQ‐12 is a cost‐effective, efficient tool that can identify patients at increased risk of death at long‐term follow‐up post‐transcatheter aortic valve replacement.
Metabolic syndrome (MetS) is a cluster of conditions that affects ∼25% of the global population, including excess adiposity, hyperglycemia, dyslipidemia, and elevated blood pressure. MetS is one of major risk factors not only for chronic diseases, but also for dementia and cognitive dysfunction, although the underlying mechanisms remain poorly understood. White matter is of particular interest in the context of MetS due to the metabolic vulnerability of myelin maintenance, and the accumulating evidence for the importance of the white matter in the pathophysiology of dementia. Therefore, we investigated the associations of MetS risk score and adiposity (combined body mass index and waist circumference) with myelin water fraction measured with myelin water imaging. In 90 cognitively and neurologically healthy adults (20–79 years), we found that both high MetS risk score and adiposity were correlated with lower myelin water fraction in late-myelinating prefrontal and associative fibers, controlling for age, sex, race, ethnicity, education and income. Our findings call for randomized clinical trials to establish causality between MetS, adiposity, and myelin content, and to explore the potential of weight loss and visceral adiposity reduction as means to support maintenance of myelin integrity throughout adulthood, which could open new avenues for prevention or treatment of cognitive decline and dementia.
Zusammenfassung
Einleitung Medizinische Fachdisziplinen sind in unterschiedlichem Ausmaß von Mangelernährung betroffen. Das nutritionDay-Projekt ermöglicht einen interdisziplinären Vergleich hinsichtlich der Prävalenz von Mangelernährung, sowie eine Übersicht über den Status quo einzelner ernährungsmedizinischer Strukturen und Prozesse in deutschen Krankenhäusern.
Methoden In diese Auswertung wurden 1865 Patient:innen von 127 Klinikstationen einbezogen, die zwischen 2016 und August 2020 am nutritionDay in Deutschland teilnahmen. Der Ernährungsstatus (BMI, Gewichtsverlust, Einstufung durch das Stationspersonal), ernährungsmedizinische Strukturen und Prozesse werden deskriptiv vergleichend für 7 Fachdisziplingruppen dargestellt.
Ergebnisse 5,5% bzw. 16,7% der teilnehmenden Patient:innen waren untergewichtig (nach WHO- bzw. ESPEN-Definition). Am häufigsten war Untergewicht in der Geriatrie (7,6% bzw. 24,0%) und am seltensten in der Neurologie (3,3% bzw. 12,0%). Unbeabsichtigter Gewichtsverlust betraf 40,1% in der Onkologie, jedoch nur 19,4% in der Neurologie. Nach Pflegeeinschätzung waren vor allem geriatrische Patient:innen mangelernährt (26,3%). 63,8% aller Stationen gaben an, eine übergeordnete Strategie für die Ernährungsversorgung zu haben (Spanne der Disziplinen 26,7–89,5%), 47,2% (35,3–61,9%) hatten eine Ansprechperson für klinische Ernährungsfragen. 88,3% (66,7–100%) führen ein Mangelernährungsscreening durch und 57,5% (44,4–84,2%) gaben routinemäßiges Wiegen bei Aufnahme an.
Diskussion Mangelernährung ist bei Krankenhauspatient:innen in Deutschland je nach Kriterium unterschiedlich häufig mit einer interdisziplinären Varianz. Die Existenz und Umsetzung ernährungsmedizinischer Strukturen und Prozesse variiert ebenfalls und ist insgesamt verbesserungswürdig. Als politische Entscheidungsgrundlage werden dringend flächendeckende Daten aus deutschen Krankenhäusern benötigt.
Multivariate meta-analysis (MMA) is a powerful statistical technique that can provide more reliable and informative results than traditional univariate meta-analysis, which allows for comparisons across outcomes with increased statistical power. However, implementing appropriate statistical methods for MMA can be challenging due to the requirement of various specific tasks in data preparation. The metavcov package aims for model preparation, data visualization, and missing data solutions to provide tools for different methods that cannot be found in accessible software. It provides sufficient constructs for estimating coefficients from other well-established packages. For model preparation, users can compute both effect sizes of various types and their variance-covariance matrices, including correlation coefficients, standardized mean difference, mean difference, log odds ratio, log risk ratio, and risk difference. The package provides a tool to plot the confidence intervals for the primary studies and the overall estimates. When specific effect sizes are missing, single imputation is available in the model preparation stage; a multiple imputation method is also available for pooling the results in a statistically principled manner from models of users' choice. The package is demonstrated in two real data applications and a simulation study to assess methods for handling missing data.
The effects of statin use for primary prevention in reducing mortality among older adults in Asian populations are unknown. This study aimed to determine whether use of statins for primary prevention was associated with a decreased risk of all-cause mortality in a Japanese older adult population. A regional, population-based, longitudinal cohort study was conducted using the Shizuoka Kokuho Database (SKDB). Data were compared between the statin-treated group and a non-statin-treated (control) group using the inverse probability of treatment weighting (IPTW) method. In the SKDB cohort aged ≥65 years, new statin use was associated with a decreased risk of all-cause mortality (hazard ratio, 0.40; 95% confidence interval [CI], 0.33–0.48) after IPTW adjustment. The risk difference for mortality at 5 years in the statin-treated group (7.9%) compared with that in the control group (92.1%) was 0.05 (95% CI, 0.04–0.06), and the number needed to treat was 21.20 (95% CI, 18.10–24.70). In conclusion, statin use for primary prevention in older adults may reduce the risk of all-cause mortality in the population without atherosclerotic disease. Furthermore, statin use for primary prevention is feasible in patients aged 75 to <85 years and in patients with comorbidities such as diabetes, or dementia.
Objective:
To focus on the intersection of perception, diagnosis, stigma, and weight bias in the management of obesity and obtain consensus on actionable steps to improve care provided for persons with obesity.
Methods:
The American Association of Clinical Endocrinology (AACE) convened a consensus conference of interdisciplinary health care professionals to discuss the interplay between the diagnosis of obesity using adiposity-based chronic disease (ABCD) nomenclature and staging, weight stigma, and internalized weight bias (IWB) with development of actionable guidance to aid clinicians in mitigating IWB and stigma in that context.
Results:
The following affirmed and emergent concepts were proposed: (1) obesity is ABCD, and these terms can be used in differing ways to communicate; (2) classification categories of obesity should have improved nomenclature across the spectrum of body mass index (BMI) using ethnic-specific BMI ranges and waist circumference (WC); (3) staging the clinical severity of obesity based on the presence and severity of ABCD complications may reduce weight-centric contribution to weight stigma and IWB; (4) weight stigma and internalized bias are both drivers and complications of ABCD and can impair quality of life, predispose to psychological disorders, and compromise the effectiveness of therapeutic interventions; (5) the presence and of stigmatization and IWB should be assessed in all patients and be incorporated into the staging of ABCD severity; and (6) optimal care will necessitate increased awareness and the development of educational and interventional tools for health care professionals that address IWB and stigma.
Conclusions:
The consensus panel has proposed an approach for integrating bias and stigmatization, psychological health, and social determinants of health in a staging system for ABCD severity as an aid to patient management. To effectively address stigma and IWB within a chronic care model for patients with obesity, there is a need for health care systems that are prepared to provide evidence-based, person-centered treatments; patients who understand that obesity is a chronic disease and are empowered to seek care and participate in behavioral therapy; and societies that promote policies and infrastructure for bias-free compassionate care, access to evidence-based interventions, and disease prevention.
This systematic meta-review evaluated the effects of exercise with and without protein interventions on muscle strength and function in older adults with sarcopenic obesity. PubMed, Cochrane Database of Systematic Reviews, Cumulative Index to Nursing and Allied Health Literature, Scopus, and SPORTDiscus databases were searched through February 2021 for relevant systematic reviews and meta-analyses with aerobic, resistance, and/or combined training interventions with and without protein supplementation in older adults ≥ 65 years with sarcopenic obesity. This meta-review showed that exercise with and without protein supplementation improved body composition (i.e., decreased percentage body fat) and functional outcomes (i.e., gait speed and grip strength). Because the current literature is limited, determining the effects of exercise and combined protein supplementation in this population requires further investigation. In the meantime, protein recommendations should align with general sarcopenia recommendations. Based upon available findings, tentative exercise recommendations to optimize health outcomes in this population are proposed.
Importance:
There is uncertainty as to which estimated glomerular filtration rate (eGFR) equation should be used among older adults.
Objective:
To compare the 5 most commonly used creatinine-based eGFR equations in older adults, quantifying the concordance among the equations, comparing their discriminative capacity in regards to 15-year mortality, and identifying sources of potential discrepancies.
Design, setting, and participants:
This cohort study used data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), a longitudinal study of adults aged 60 years or older in Sweden. Participants were recruited between 2001 and 2004 and followed up for mortality until December 2016. Participants missing creatinine values were excluded. Data were originally analyzed March through July 2022, and were rerun in January 2023.
Exposures:
Five creatinine-based equations were considered: Modification of Diet in Renal Disease (MDRD), 2009 Chronic Kidney Disease Epidemiological Collaboration (CKD-EPI), Revised Lund-Malmö (RLM), Berlin Initiative Study (BIS), and European Kidney Function Consortium (EKFC).
Main outcomes and measures:
Concordance between equations was quantified using Cohen κ. Discriminative capacity for mortality was quantified using area under the receiver operating characteristic curve (AUC) and the Harrel C statistic. Calf circumference, body mass index (BMI), and age were explored as correlates of discrepancies.
Results:
The study sample consisted of 3094 older adults (1972 [63.7%] female; median [IQR] age, 72 [66-81] years). Cohen κ between dyads of equations ranged from 0.42 to 0.91, with poorest concordance between MDRD and BIS, and best between RLM and EKFC. MDRD and CKD-EPI provided higher estimates of GFR compared with the other equations. The best mix of AUC and Harrel C statistic was observed for BIS (0.80 and 0.73, respectively); however, the prognostic accuracy for death decreased among those aged over 78 years and those with low calf circumference. Differences between equations were inconsistent across levels of calf circumference, BMI, and age.
Conclusions and relevance:
In this cohort study, we found that eGFR equations were not interchangeable when assessing kidney function. BIS outperformed other equations in predicting mortality; however, its discriminative capacity was reduced in subgroup analyses. Clinicians should consider these discrepancies when monitoring kidney function in old age.
Individuals who require home enteral nutrition (EN)-and, subsequently, their families-undergo major changes in family dynamics. They would benefit from an approach that provides ongoing nutrition, hydration, enteral access, and quality of life assessments from knowledgeable clinicians. Most individuals and families will be new to the enteral access device, handling and administration of the nutrition formula, medication delivery through feeding tubes, and troubleshooting associated complications. Educational sessions are typically given but may not be comprehensive enough to meet all needs, especially in the first weeks to months of home EN therapy. Quality of life assessments obtained in the early stages of EN support would help clinicians identify and focus on areas that need special attention for that individual and family. Ongoing clinical care is essential because over time, the nutrition prescription, delivery methods and schedules typically require alterations and feeding tubes need to be assessed and replaced. In addition to these important physical dimensions of care, attention should be placed on the psychological, cognitive, social, and ethical aspects of life for these individuals and their families.
Objective:
Prior studies report conflicting results about the association between lithium use and all-cause mortality. In addition, data are scarce on this association among older adults with psychiatric disorders. In this report, we sought to examine the associations of lithium use with all-cause mortality and specific causes of death (i.e., due to cardiovascular disorder, non-cardiovascular disease, accident, or suicide) among older adults with psychiatric disorders during a 5-year follow-up period.
Methods:
In this observational epidemiological study, we used data from 561 patients belonging to a Cohort of individuals with Schizophrenia or Affective disorders aged 55-years or more (CSA). Patients taking lithium at baseline were first compared to patients not taking lithium, and then to patients taking (i) antiepileptics and (ii) atypical antipsychotics in sensitivity analyses. Analyses were adjusted for socio-demographic (e.g., age, gender), clinical characteristics (e.g., psychiatric diagnosis, cognitive functioning), and other psychotropic medications (e.g. benzodiazepines).
Results:
There was no significant association between lithium use and all-cause mortality [AOR=1.12; 95%CI=0.45-2.79; p=0.810] or disease-related mortality [AOR=1.37; 95%CI=0.51-3.65; p=0.530]. None of the 44 patients taking lithium died from suicide, whereas 4.0% (N=16) of patients not receiving lithium did.
Conclusion:
These findings suggest that lithium may not be associated with all-cause or disease-related mortality and might be associated with reduced risk of suicide in this population. They argue against the underuse of lithium as compared with antiepileptics and atypical antipsychotics among older adults with mood disorders.
Background:
The association between obesity and depressive symptoms has been described in the literature, but there is a scarcity of longitudinal data. This study aimed to verify the association between body mass index (BMI) and waist circumference and the incidence of depressive symptoms over a 10-year follow-up in a cohort of older adults.
Methods:
Data from the first (2009-2010), second (2013-2014), and third (2017-2019) waves of the EpiFloripa Aging Cohort Study were used. Depressive symptoms were assessed by the 15-item Geriatric Depression Scale (GDS-15) and classified in significant depressive symptoms for those with ≥6 points. The Generalized Estimating Equations model was used to estimate the longitudinal association between BMI and waist circumference and depressive symptoms across a 10-year follow-up.
Results:
The incidence of depressive symptoms (N = 580) was 9.9 %. The relationship between BMI and the incidence of depressive symptoms in older adults followed a U-shaped curve. Older adults with obesity had an incidence relative ratio of 76 % (IRR = 1.24, p = 0.035) for increasing the score of depressive symptoms after 10 years, compared to those with overweight. The higher category of waist circumference (Male: ≥102; Female: ≥88 cm) was associated with depressive symptoms (IRR = 1.09, p = 0.033), only in a non-adjusted analysis.
Limitations:
Relatively high follow-up dropout rate; Few individuals in the underweight BMI category; BMI must be considered with caution because it does not measure only fat mass.
Conclusions:
Obesity was associated with the incidence of depressive symptoms when compared with overweight in older adults.
BACKGROUND: Body mass index (BMI) is calculated by dividing a person's height in meters by their weight in kilograms and is always expressed in kg/m2. BMI is a reliable risk indicator for various diseases that can develop due to a higher percentage of body fat. There is a possibility of developing certain medical conditions including heart disease, high blood pressure, type 2 diabetes, gallstones, breathing problems and some malignancies that increase with BMI.
OBJECTIVE: This study seeks to identify the trends in the Body Mass Index (BMI) of women visiting Better Life Primary Health Care Centre in Ondo City, Ondo State, Nigeria for various healthcare services.
METHODOLOGY: Seventy (70) women who participated in this study were randomly selected using systematic random sampling. Their necessary information was obtained using a prepared record sheet to collect their data. Results were analysed using SPSS version 21.
RESULTS: The results show mean age of the respondents is 30.50 + 6.52921 21 (30%) were between 31 – 35 years, 21 (30%) were traders, 36 (51%) had tertiary education, 39 (55.7%) were between 151 – 160 cm in height. 32 (45.7%) were between 60 – 80 kg in weight and 30 (42.9%) had normal weight, respectively. There is statistically significant association between Weight and Body Mass Index (p-value = 0.000).
CONCLUSION: Body Mass Index (BMI) calculation is an essential tool that can help in identifying one of the significant public health issues that leads to obesity; a predisposing factor to various medical conditions like hypertension, musculoskeletal issues, heart diseases, cancers, and lots more. It is therefore important to incorporate BMI calculation into routine checks to prevent or reduce health burdens that may arise from obesity.
Keywords: Body, Mass, Index, Women, Healthcare services
Interoception is the detection of signals that arise from within the body. Interoceptive sensitivity has been found to be associated with affect and cognition among younger adults, and examination of these relationships in older adult samples is beginning to emerge. Here, we take an exploratory approach to determine how demographic, affective, and cognitive variables relate to interoceptive sensitivity in neurologically normal older adults, aged 60–91 years old. Ninety-one participants completed a comprehensive neuropsychological battery, self-report questionnaires, and a heartbeat counting task to measure interoceptive sensitivity. Our findings revealed several relationships: 1) interoceptive sensitivity was inversely correlated with measures of positive emotionality: participants with higher interoceptive sensitivity tended to have lower levels of positive affect and trait extraversion; 2) interoceptive sensitivity was found to positively correlate with cognition: participants who performed better on the heartbeat-counting task also tended to perform better on a measure of delayed verbal memory; and 3) when examining the predictors of interoceptive sensitivity in a single hierarchical regression model, higher interoceptive sensitivity was related to: higher time estimation, lower positive affect, lower extraversion, and higher verbal memory. In total, the model accounted for 38% of the variability in interoceptive sensitivity (R² = .38). These results suggest that, among older adults, interoceptive sensitivity is facilitative for aspects of cognition but perhaps disruptive for certain aspects of emotional experience.
IntroductionThe importance of a nutrition scoring system, including the geriatric nutritional risk index (GNRI), was reported as an objective tool widely used to assess nutritional status in patients with inflammatory disease, chronic heart failure, and chronic liver disease. However, studies on the relationship between GNRI and the prognosis in patients who have undergone initial hepatectomy have been limited. Thus, we conducted a multi-institutional cohort study to clarify the relationship between GNRI and long-term outcomes for hepatocellular carcinoma (HCC) patients after such a procedure.Methods
Data from 1,494 patients who underwent initial hepatectomy for HCC between 2009 and 2018 was retrospectively collected from a multi-institutional database. The patients were divided into two groups according to GNRI grade (cutoff: 92), and their clinicopathological characteristics and long-term results were compared.ResultsOf the 1,494 patients, the low-risk group (≥ 92; N = 1,270) was defined as having a normal nutritional status. Meanwhile, low GNRI (< 92; N = 224) were divided into malnutrition as the high-risk group. Multivariate analysis identified seven prognostic factors of poor overall survival (higher tumor markers; α-fetoprotein (AFP) and des-γ-carboxy protein [DCP], higher ICG-R15 levels, larger tumor size, multiple tumors, vascular invasion, and lower GNRI and eight prognostic factors of high recurrence (HCV antibody positive, higher ICG-R15 levels, higher tumor markers such as AFP and DCP, greater bleeding, multiple tumors, vascular invasion, and lower GNRI).Conclusions
In patients with HCC, preoperative GNRI predicts poorer overall survival and high recurrence.
Health literacy (HL) is an important decision factor for health. Both low HL and low physical function cause adverse events in cardiovascular disease patients, but their relationship is not well documented. To clarify the relationship between HL and physical function of patients participating in cardiac rehabilitation and calculate the cutoff value of the 14-item HL scale (HLS) for low handgrip strength, this multicenter clinical study named the Kobe-Cardiac Rehabilitation project for people around the World (K-CREW) was conducted among four affiliated hospitals with patients who underwent cardiac rehabilitation. We used the 14-item HLS to assess HL, and the main outcomes were handgrip strength and Short Physical Performance Battery (SPPB) score. The study included 167 cardiac rehabilitation patients with a mean age of 70.5 ± 12.8 years, and the ratio of males was 74%. Among them, 90 patients (53.9%) had low HL and scored significantly lower in both handgrip strength and SPPB. Multiple linear regression analysis revealed that HL was a determinant factor (β = 0.118, p = 0.04) for handgrip strength. Receiver operating characteristic analysis revealed the cutoff value of the 14-item HLS for screening for low handgrip strength was 47.0 points, and the area under the curve was 0.73. This study showed that HL was significantly associated with handgrip strength and SPPB in cardiac rehabilitation patients and suggests the possibility of early screening for low HL to improve physical function in cardiac rehabilitation patients with low HL.
The obesity epidemic in aging populations poses significant public health concerns for greater morbidity and mortality risk. Age-related increased adiposity is multifactorial and often associated with reduced lean body mass. The criteria used to define obesity by body mass index in younger adults may not appropriately reflect age-related body composition changes. No consensus has been reached on the definition of sarcopenic obesity in older adults. Lifestyle interventions are generally recommended as initial therapy; however, these approaches have limitations in older adults. Similar benefits in older compared with younger adults are reported with pharmacotherapy, however, large randomized clinical trials in geriatric populations are lacking.
Estimates of mortality differences by body mass index (BMI) are likely biased by: (1) confounding bias from heterogeneity in body shape; (2) positive survival bias in high-BMI samples due to recent weight gain; and (3) negative survival bias in low-BMI samples due to recent weight loss. I investigate these sources of bias in the National Health and Nutrition Examination Survey (NHANES) 1988-94 and 1999-2006 linked to mortality up to 2015 (17,784 cases; 4,468 deaths). I use Cox survival models to estimate BMI differences in all-cause mortality risks among adults aged [45-85) in the United States. I test for age-based differences in BMI-mortality associations and estimate functional forms of the association using nine BMI levels. Estimates of the BMI-mortality association in NHANES data are significantly affected by all three biases, and obesity-mortality associations adjusted for bias are substantively strong at all ages. The mortality consequences of overweight and obesity have likely been underestimated, especially at older ages.
This chapter focuses on the influences of lifelong sexual practice on health in later life. Human life expectancy has been continuously increasing in the past decades. Despite the recent decline in life expectancy in the United States, the number of older adults is rising globally, and our society is facing rapid aging overall. Sexual health is important for human health and well-being. Often being overlooked previously, sexuality and sexual health among older adults have become more important than ever. There are many forms of sexual behavior and types of sexual activity. In this chapter, we will discuss the positive influence of lifelong sexual practice on the physical, emotional, social, and cognitive aspects of health among older adults and the potential risk of sexually transmitted infections. Given that general health condition declines with age, we also discuss the health issues among older women and men that may affect their sexual functioning and satisfaction. Lastly, we specifically discuss sexual health among the underserved population, including older adults who self-identified as sexual and gender minorities and those who live with chronic conditions. In summary, it is essential not to assume that older individuals are indifferent to intimacy and sexual pleasure. Although sexual health-related problems in older adults are frequent, they are scarcely brought to the attention of physicians and other healthcare professionals. Thus, it is important to integrate a safe and nonjudgmental system within healthcare professionals where all sexually active older individuals have access to education, counseling, and treatment related to sexual health and safe sexual practices.
In older adults sexuality is often a taboo topic focusing on sexual dysfunction and is seldom seen as something that contributes to one’s general well-being and health. A healthy lifestyle with sufficient physical activity, a healthy diet, an absence of smoking, and none to moderate alcohol consumption are significant ways of promoting and supporting satisfying sex. Body weight reduction also influences sexual activity and is a direct consequence of physical activity and nutritional behaviour. This happens in various ways - on the one hand it optimises one’s lifestyle with positive physiological effects (e.g. change in endocrine factors). On the other hand, it enhances physical fitness, prevents chronic diseases, and is also effective in sexual dysfunction therapy. The following chapter demonstrates the association between lifestyle factors and sexuality through recent studies. It describes the beneficial effects of various training methods (aerobics, muscle-strengthening exercises, stretching, and yoga). It summarises the association between diet (focusing on protein intake, fruit intake, and supplements) and sexuality and describes the positive effects of non-smoking and moderate alcohol consumption on sexuality. Actual literature is presented in every subchapter, recommendations are shown, and some practical hints are given on optimising one’s lifestyle and sexual activity. The main messages are summarised as take-home messages.
Body mass index (BMI) is widely used as a first-line screening biomarker for nutritional status assessment. The advantages of BMI are its simplicity, low cost, and non-invasiveness. However, this biomarker has a number of limitations, which lead to low sensitivity in the diagnosis of both malnutrition and obesity; for example, more than half of the people with a high percentage of body fat (e.g., >30%) are diagnosed as being in the BMI range for a normal weight. The shortcomings of BMI as a biomarker of malnutrition depend on: (a) the slow effect of decreased food intake on its value and (b) its weak correlation with biochemical and immunological parameters of malnutrition. Whereas, the limitations of BMI as a biomarker of obesity are related to: (a) an inability to distinguish between fat and fat-free (lean) body mass; (b) a failure to determine fat distribution; (c) a dependence on the accuracy of reported or measured height; and (d) the influence of age, gender, and comorbidities on the accuracy of the cut-offs used in the diagnosis of obesity. Nevertheless, BMI correlates with: (a) central body fat distribution; (b) laboratory biomarkers of metabolic (e.g., blood glucose, lipids, uric acid), inflammatory (e.g., C-reactive protein, interleukin-6, and tumor necrosis factor alpha), and endothelial (e.g., VEGF and ICAM) abnormalities. BMI is also useful as: (c) a risk factor (biomarker) in the development of a number of health conditions, such as diabetes mellitus, hypertension, infectious disease, and psoriasis; (d) as a prognostic factor for all-cause and cardiovascular mortality, in-hospital all-cause mortality, surgery complications and outcomes, hospital-acquired (nosocomial) infections, length of in-hospital stay, and risk of readmission; as well as (e) a biomarker for monitoring the clinical and metabolic effects of interventions on weight reduction, including bariatric surgery. This chapter presents an overview of scientific works related to the use of BMI as a biomarker for various clinical disorders and their clinical course.
Background: Guidelines for optimal weight in older persons are limited by uncertainty about the ideal body mass index (BMI) or the usefulness of alternative anthropometric measures.
Objective: We investigated the association of BMI (in kg/m²), waist circumference, and waist-hip ratio (WHR) with mortality and cause-specific mortality.
Design: Subjects aged ≥75 y (n = 14 833) from 53 family practices in the United Kingdom underwent a health assessment that included measurement of BMI and waist and hip circumferences; they also were followed up for mortality.
Results: During a median follow-up of 5.9 y, 6649 subjects died (46% of circulatory causes). In nonsmoking men and women (90% of the cohort), compared with the lowest quintile of BMI (<23 in men and <22.3 in women), adjusted hazard ratios (HRs) for mortality were <1 for all other quintiles of BMI (P for trend = 0.0003 and 0.0001 in men and women, respectively). Increasing WHR was associated with increasing HRs in men and women (P for trend = 0.008 and 0.0002, respectively). BMI was not associated with circulatory mortality in men (P for trend = 0.667) and was negatively associated in women (P for trend = 0.004). WHR was positively related to circulatory mortality in both men and women (P for trend = 0.001 and 0.005, respectively). Waist circumference was not associated with all-cause or circulatory mortality.
Conclusions: Current guidelines for BMI-based risk categories overestimate risks due to excess weight in persons aged ≥75 y. Increased mortality risk is more clearly indicated for relative abdominal obesity as measured by high WHR.
Context The prevalence of obesity and overweight increased in the United States
between 1978 and 1991. More recent reports have suggested continued increases
but are based on self-reported data.Objective To examine trends and prevalences of overweight (body mass index [BMI]
≥25) and obesity (BMI ≥30), using measured height and weight data.Design, Setting, and Participants Survey of 4115 adult men and women conducted in 1999 and 2000 as part
of the National Health and Nutrition Examination Survey (NHANES), a nationally
representative sample of the US population.Main Outcome Measure Age-adjusted prevalence of overweight, obesity, and extreme obesity
compared with prior surveys, and sex-, age-, and race/ethnicity–specific
estimates.Results The age-adjusted prevalence of obesity was 30.5% in 1999-2000 compared
with 22.9% in NHANES III (1988-1994; P<.001).
The prevalence of overweight also increased during this period from 55.9%
to 64.5% (P<.001). Extreme obesity (BMI ≥40)
also increased significantly in the population, from 2.9% to 4.7% (P = .002). Although not all changes were statistically significant,
increases occurred for both men and women in all age groups and for non-Hispanic
whites, non-Hispanic blacks, and Mexican Americans. Racial/ethnic groups did
not differ significantly in the prevalence of obesity or overweight for men.
Among women, obesity and overweight prevalences were highest among non-Hispanic
black women. More than half of non-Hispanic black women aged 40 years or older
were obese and more than 80% were overweight.Conclusions The increases in the prevalences of obesity and overweight previously
observed continued in 1999-2000. The potential health benefits from reduction
in overweight and obesity are of considerable public health importance.
In this study, we analyzed age variation in the association between obesity status and US adult mortality risk. Previous studies have found that the association between obesity and mortality risk weakens with age. We argue that existing results were derived from biased estimates of the obesity-mortality relationship because models failed to account for confounding influences from respondents' ages at survey and/or cohort membership. We employed a series of Cox regression models in data from 19 cross-sectional, nationally representative waves of the US National Health Interview Survey (1986-2004), linked to the National Death Index through 2006, to examine age patterns in the obesity-mortality association between ages 25 and 100 years. Findings suggest that survey-based estimates of age patterns in the obesity-mortality relationship are significantly confounded by disparate cohort mortality and age-related survey selection bias. When these factors are accounted for in Cox survival models, the obesity-mortality relationship is estimated to grow stronger with age. © 2013 © The Author 2013. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected]
/* */
Estimates of the relative mortality risks associated with normal weight, overweight, and obesity may help to inform decision making in the clinical setting.
To perform a systematic review of reported hazard ratios (HRs) of all-cause mortality for overweight and obesity relative to normal weight in the general population.
PubMed and EMBASE electronic databases were searched through September 30, 2012, without language restrictions.
Articles that reported HRs for all-cause mortality using standard body mass index (BMI) categories from prospective studies of general populations of adults were selected by consensus among multiple reviewers. Studies were excluded that used nonstandard categories or that were limited to adolescents or to those with specific medical conditions or to those undergoing specific procedures. PubMed searches yielded 7034 articles, of which 141 (2.0%) were eligible. An EMBASE search yielded 2 additional articles. After eliminating overlap, 97 studies were retained for analysis, providing a combined sample size of more than 2.88 million individuals and more than 270,000 deaths.
Data were extracted by 1 reviewer and then reviewed by 3 independent reviewers. We selected the most complex model available for the full sample and used a variety of sensitivity analyses to address issues of possible overadjustment (adjusted for factors in causal pathway) or underadjustment (not adjusted for at least age, sex, and smoking).
Random-effects summary all-cause mortality HRs for overweight (BMI of 25-<30), obesity (BMI of ≥30), grade 1 obesity (BMI of 30-<35), and grades 2 and 3 obesity (BMI of ≥35) were calculated relative to normal weight (BMI of 18.5-<25). The summary HRs were 0.94 (95% CI, 0.91-0.96) for overweight, 1.18 (95% CI, 1.12-1.25) for obesity (all grades combined), 0.95 (95% CI, 0.88-1.01) for grade 1 obesity, and 1.29 (95% CI, 1.18-1.41) for grades 2 and 3 obesity. These findings persisted when limited to studies with measured weight and height that were considered to be adequately adjusted. The HRs tended to be higher when weight and height were self-reported rather than measured.
Relative to normal weight, both obesity (all grades) and grades 2 and 3 obesity were associated with significantly higher all-cause mortality. Grade 1 obesity overall was not associated with higher mortality, and overweight was associated with significantly lower all-cause mortality. The use of predefined standard BMI groupings can facilitate between-study comparisons.
Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30
Two methods for point and interval estimation of relative risk for log-linear exposure-response relations in meta-analyses of published ordinal categorical exposure-response data have been proposed. The authors compared the results of a meta-analysis of published data using each of the 2 methods with the results that would be obtained if the primary data were available and investigated the circumstances under which the approximations required for valid use of each meta-analytic method break down. They then extended the methods to handle nonlinear exposure-response relations. In the present article, methods are illustrated using studies of the relation between alcohol consumption and colorectal and lung cancer risks from the ongoing Pooling Project of Prospective Studies of Diet and Cancer. In these examples, the differences between the results of a meta-analysis of summarized published data and the pooled analysis of the individual original data were small. However, incorrectly assuming no correlation between relative risk estimates for exposure categories from the same study gave biased confidence intervals for the trend and biased P values for the tests for nonlinearity and between-study heterogeneity when there was strong confounding by other model covariates. The authors illustrate the use of 2 publicly available user-friendly programs (Stata and SAS) to implement meta-analysis for dose-response data.
To describe the relationship of body mass index and mortality in older adults, examining the influence of sex and cardiovascular morbidity.
Sixteen-year cohort of a population sample of 1,008 people aged 65 and over. BMI mortality hazard ratios are estimated controlling for age, sex, education, physical activity, smoking, chronic conditions, and ADL (activities of daily living) disability.
At baseline the median BMI is 26.8 (Interquartile range: 24.2-29.7 Kg/m(2)). Findings show that during 16 years there were 672 deaths. The U-shaped curve of the mortality hazard by BMI is wide. The minimum mortality occur at BMI = 30.5 Kg/m(2). Findings show that men had lower mortality risk with increasing BMI and that cardiovascular disease was associated with high mortality in the low-BMI category.
Underweight is a risk factor for mortality among elderly people, whereas overweight and mild obesity are associated with the lowest mortality particularly among men and those with cardiovascular morbidity.
The impact of body mass index (BMI; kg/m(2)) and waist circumference (WC) on mortality in elderly individuals is controversial and previous research has largely focused on obesity.
With special attention to the lower BMI categories, associations between BMI and both total and cause-specific mortality were explored in 7604 men and 9107 women aged ≥ 65 years who participated in the Tromsø Study (1994-1995) or the North-Trøndelag Health Study (1995-1997). A Cox proportional hazards model adjusted for age, marital status, education and smoking was used to estimate HRs for mortality in different BMI categories using the BMI range of 25-27.5 as a reference. The impact of each 2.5 kg/m(2) difference in BMI on mortality in individuals with BMI < 25.0 and BMI ≥ 25.0 was also explored. Furthermore, the relations between WC and mortality were assessed.
We identified 7474 deaths during a mean follow-up of 9.3 years. The lowest mortality was found in the BMI range 25-29.9 and 25-32.4 in men and women, respectively. Mortality was increased in all BMI categories below 25 and was moderately increased in obese individuals. U-shaped relationships were also found between WC and total mortality. About 40% of the excess mortality in the lower BMI range in men was explained by mortality from respiratory diseases.
BMI below 25 in elderly men and women was associated with increased mortality. A modest increase in mortality was found with increasing BMI among obese men and women. Overweight individuals (BMI 25-29.9) had the lowest mortality.
A high body-mass index (BMI, the weight in kilograms divided by the square of the height in meters) is associated with increased mortality from cardiovascular disease and certain cancers, but the precise relationship between BMI and all-cause mortality remains uncertain.
We used Cox regression to estimate hazard ratios and 95% confidence intervals for an association between BMI and all-cause mortality, adjusting for age, study, physical activity, alcohol consumption, education, and marital status in pooled data from 19 prospective studies encompassing 1.46 million white adults, 19 to 84 years of age (median, 58).
The median baseline BMI was 26.2. During a median follow-up period of 10 years (range, 5 to 28), 160,087 deaths were identified. Among healthy participants who never smoked, there was a J-shaped relationship between BMI and all-cause mortality. With a BMI of 22.5 to 24.9 as the reference category, hazard ratios among women were 1.47 (95 percent confidence interval [CI], 1.33 to 1.62) for a BMI of 15.0 to 18.4; 1.14 (95% CI, 1.07 to 1.22) for a BMI of 18.5 to 19.9; 1.00 (95% CI, 0.96 to 1.04) for a BMI of 20.0 to 22.4; 1.13 (95% CI, 1.09 to 1.17) for a BMI of 25.0 to 29.9; 1.44 (95% CI, 1.38 to 1.50) for a BMI of 30.0 to 34.9; 1.88 (95% CI, 1.77 to 2.00) for a BMI of 35.0 to 39.9; and 2.51 (95% CI, 2.30 to 2.73) for a BMI of 40.0 to 49.9. In general, the hazard ratios for the men were similar. Hazard ratios for a BMI below 20.0 were attenuated with longer-term follow-up.
In white adults, overweight and obesity (and possibly underweight) are associated with increased all-cause mortality. All-cause mortality is generally lowest with a BMI of 20.0 to 24.9.
To investigate the relationship between mortality and BMI in older people, taking into account other established mortality risk factors.
A total of 3,646 French community dwellers aged 65 years and older from PAQUID cohort study were included. Cox proportional-hazards analysis was used to assess association between BMI and mortality.
Death occurred in 54.1% of the cohort more than 13 years: 68.99% of the underweight (BMI <19), 52.13% of the obese (BMI >30), 51.66% of the overweight (BMI 25-30), and 51.79% of the reference participants (BMI 22-25) died.The relative risk of death as a function of BMI, adjusted for gender and age, formed a U-shaped pattern, with larger risks associated with lower BMI (<22.0) and for BMI of 25.0 to 30.0 and BMI >/=30. (BMI 22.0-24.9 was the reference.) After adjustment for demographic factors, smoking history, and comorbidity, increased mortality risk persisted in underweight older people, BMI <18.5 and BMI 18.5-22 (respectively, HR = 1.45, 95% CI 1.17-1.78; HR = 1.27, 95% CI 1.12-1.43) compared with reference. Overweight (BMI 25-29.9) and obesity (>/=30) were not associated with increased mortality compared with the reference category (respectively, HR = 0.98, 95% IC 0.88-1.10; HR = 1.06, 95% IC 0.89-1.27). Similar relationships persisted for disabled participant. For nondisabled participant disability did not alter the associations for BMI of 25.0 and higher but for BMI less than 22.0, the risks become insignificantly different from those for the reference group.
BMI below 22 kg/ m(2) is a risk factor for 13-year mortality in older people, but our findings suggest that overweight and obesity may not be associated to mortality after adjustment for established mortality risk factors.
Low body mass index (BMI) and low cardiorespiratory fitness (CRF) are independently associated with increased mortality in the elderly. However, interactions among BMI, CRF, and mortality in older persons have not been adequately explored.
Hazard ratios (HRs) were calculated for predetermined strata of BMI and CRF. Independent and joint associations of CRF, BMI, and all-cause mortality were assessed by Cox proportional hazards analyses in a prospective cohort of 981 healthy men aged at least 65 years (mean age [+/-SD], 71 [+/-5] years; range, 65-88 years) referred for exercise testing during 1987-2003.
During a mean follow-up of 6.9 +/- 4.4 years, a total of 208 patients died. Multivariate relative risks (95% confidence interval [CI]) of mortality across BMI groups of <20.0, 20.0-25.0, 25.0-29.9, 30.0-34.9, and > or =35.0 were 2.51 (1.26-4.98), 1.0 (reference), 0.66 (0.48-0.90), 0.50 (0.31-0.78), and 0.44 (0.20-0.97), respectively, and across CRF groups of <5.0, 5.0-8.0, and >8.0 metabolic equivalents were 1.0 (reference), 0.56 (0.40-0.78), and 0.39 (0.26-0.58), respectively. In a separate analysis of within-strata CRF according to BMI grouping, the lowest mortality risk was observed in obese men with high fitness (HR [95% CI] 0.26 [0.10-0.69]; p = .007).
In this cohort of elderly male veterans, we observed independent and joint inverse relations of BMI and CRF to mortality. This warrants further investigation of fitness, fatness, and mortality interactions in older persons.
Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses.
Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews.
Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision.
In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias.
A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution.
The US Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults set the body mass index (BMI; weight in kilograms divided by the square of height in meters) of 25 as the upper limit of ideal weight for all adults regardless of age. However, the prognostic importance of overweight and obesity in elderly persons (>/=65 years) is controversial. We sought to analyze the guidelines in the context of currently available evidence that is relevant to older adults.
We searched MEDLINE for all English-language studies of the association between BMI and all-cause or cardiovascular mortality or coronary heart disease events from January 1966 through October 1999. Additional pertinent articles were identified through bibliographies of the MEDLINE articles. We selected studies for detailed review if they reported on the association between BMI and mortality for nonhospitalized subjects who were 65 years or older and had been followed up for at least 3 years. We controlled for age, smoking, and baseline health status. Of the 444 screened articles, 13 were selected to assess the guidelines. We extracted information regarding publication year, study design, population, recruitment period, follow-up duration, number of subjects, sex, age range, inclusion and exclusion criteria, and statistical models, including variables and end points.
These data do not support the BMI range of 25 to 27 as a risk factor for all-cause and cardiovascular mortality among elderly persons. The results were not substantially different for men and women. Most studies showed a negative or no association between BMI and all-cause mortality. Three studies indicated overweight (BMI >/=27) as a significant prognostic factor for all-cause and cardiovascular mortality among 65- to 74-year-olds, and one study showed a significant positive association between overweight (BMI >/=28) and all-cause mortality among those 75 years or older. Higher BMI values were consistent with a smaller relative mortality risk in elderly persons compared with young and middle-aged populations.
Federal guideline standards for ideal weight (BMI 18.7 to <25) may be overly restrictive as they apply to the elderly. Studies do not support overweight, as opposed to obesity, as conferring an excess mortality risk. Future guidelines should consider the evidence for specific age groups when establishing standards for healthy weight.
To examine the relationship between body mass index (BMI) at age 70, weight change between age 70 and 75, and 15 y mortality.
Cohort study of 70-y-olds.
Geriatric Medicine Department, Göteborg University, Sweden.
A total of 2628 (1225 males and 1403 females) 70-y-olds examined in 1971--1981 in Gothenburg, Sweden.
The relative risks (RRs) for 15 y mortality were highest in the lowest BMI quintiles of males 1.20 (95% CI 0.96--1.51) and females 1.49 (95% CI 1.14--1.96). In non-smoking males, no significant differences were observed across the quintiles for 5, 10 and 15 y mortality. In non-smoking females, the highest RR (1.58, 95% CI 1.15--2.16) for 15 y mortality was in the lowest quintile. After exclusion of first 5 y death, no excess risks were found in males for following 5 and 10 y mortality across the quintiles. In females, a U-shaped relation was observed after such exclusions. BMI ranges with lowest 15 y mortality were 27--29 and 25--27 kg/m(2) in non-smoking males and females, respectively. A weight loss of > or = 10% between age 70 and 75 meant a significantly higher risk for subsequent 5 and 10 y mortality in both sexes relative to individuals with 'stable' weights.
Low BMI and weight loss are risk factors for mortality in the elderly and smoking habits did not significantly modify that relationship. The BMI ranges with lowest risks for 15 y mortality are relatively higher in elderly. Exclusion of early deaths from the analysis modified the weight-mortality relationship in elderly males but not in females.
This paper presents a command, glst, for trend estimation across different exposure levels for either single or multiple summarized case-control, incidence-rate, and cumulative incidence data. This approach is based on constructing an approximate covariance estimate for the log relative risks and estimating a corrected linear trend using generalized least squares. For trend analysis of multiple studies, glst can estimate fixed- and random-effects metaregression models. Copyright 2006 by StataCorp LP.
For the elderly, the association between waist circumference (WC) and mortality considering body mass index (BMI) remains unclear, and thereby also the evidence base for using these anthropometric measures in clinical practice. This meta-analysis examined the association between WC categories and (cause-specific) mortality within BMI categories. Furthermore, the association of continuous WC with lowest and increased mortality risks was examined.
Age- and smoking-adjusted relative risks (RRs) of mortality associated with WC-BMI categories and continuous WC (including WC and WC(2)) were calculated by the investigators and pooled by means of random-effects models.
During a 5-year-follow-up of 32 678 men and 25 931 women, we ascertained 3318 and 1480 deaths, respectively. A large WC (men: ≥102 cm, women: ≥88 cm) was associated with increased all-cause mortality RRs for those in the 'healthy' weight {1.7 [95% confidence interval (CI): 1.2-2.2], 1.7 (95% CI: 1.3-2.3)}, overweight [1.1(95% CI: 1.0-1.3), 1.4 (95%: 1.1-1.7)] and obese [1.1 (95% CI: 1.0-1.3), 1.6 (95% CI: 1.3-1.9)] BMI category compared with the 'healthy' weight (20-24.9 kg/m(2)) and a small WC (<94 cm, men; <80 cm, women) category. Underweight was associated with highest all-cause mortality RRs in men [2.2 (95% CI: 1.8-2.8)] and women [2.3 (95% CI: 1.8-3.1]. We found a J-shaped association for continuous WC with all-cause, cardiovascular (CVD) and cancer, and a U-shaped association with respiratory disease mortality (P < 0.05). An all-cause (CVD) mortality RR of 2.0 was associated with a WC of 132 cm (123 cm) in men and 116 cm (105 cm) in women.
Our results showed increased mortality risks for elderly people with an increased WC-even across BMI categories- and for those who were classified as 'underweight' using BMI. The results provide a solid basis for re-evaluation of WC cut-points in ageing populations.
A WHO expert consultation addressed the debate about interpretation of recommended body-mass index (BMI) cut-off points for determining overweight and obesity in Asian populations, and considered whether population-specific cut-off points for BMI are necessary. They reviewed scientific evidence that suggests that Asian populations have different associations between BMI, percentage of body fat, and health risks than do European populations. The consultation concluded that the proportion of Asian people with a high risk of type 2 diabetes and cardiovascular disease is substantial at BMIs lower than the existing WHO cut-off point for overweight (greater than or equal to25 kg/m(2)). However, available data do not necessarily indicate a clear BMI cut-off point for all Asians for overweight or obesity. The cut-off point for observed risk varies from 22 kg/m(2) to 25 kg/m(2) in different Asian populations; for high risk it varies from 26 kg/m(2) to 31 kg/m(2). No attempt was made, therefore, to redefine cut-off points for each population separately. The consultation also agreed that the WHO BMI cut-off points should be retained as international classifications. The consultation identified further potential public health action, points (23.0, 27.5, 32.5, and 37.5 kg/m(2)) along the continuum of BMI, and proposed methods by which countries could make decisions about the definitions of increased risk for their population.
The relation between relative weight and health differs between young and old. In older populations, weight change may cloud the association between a single relative weight and health outcomes. To determine whether weight or weight change is a more important determinant of mortality in a population of older adults, the authors analyzed data from the Systolic Hypertension in the Elderly Program (1984-1990), a randomized clinical trial testing the efficacy of antihypertensive drug treatment to reduce the risk of stroke in older adults (aged 60 years or more) with isolated systolic hypertension. After adjustment for covariates, an average annualized weight loss of at least 1.6 kg/year (odds ratio = 4.9), a weight loss between 1.6 and 0.7 kg/year (odds ratio = 1.7), a weight gain of more than 0.5 kg/year (odds ratio = 2.4), and a baseline body mass index of less than 23.6 (odds ratio = 1.4) all had a significant (p < 0.05) association with all-cause mortality compared with a referent group that was weight stable and of intermediate body mass index (23.6 to <28.0 kg/m 2 ) and weight change (-0.7 to <0.5 kg/year). The authors conclude that, in older adults, dynamic measures (e.g., annualized weight change) of weight change predict mortality better than do static weight measures (e.g., baseline body mass index). Even in those with high or low baseline body mass index, weight stability is associated with a lower mortality risk.
To examine how body mass index (BMI) and change in BMI are associated with mortality in old (70–79) and very old (≥80) individuals.
Pooled data from three multidisciplinary prospective population-based studies: OCTO-twin, Gender, and NONA.
Sweden.
Eight hundred eighty-two individuals aged 70 to 95.
BMI was calculated from measured height and weight as kg/m2. Information about survival status and time of death was obtained from the Swedish Civil Registration System.
Mortality hazard was 20% lower for the overweight group than the normal–underweight group (relative risk (RR) = 0.80, P = .011), and the mortality hazard for the obese group did not differ significantly from that of the normal–underweight group (RR = 0.93, P = .603), independent of age, education, and multimorbidity. Furthermore, mortality hazard was 65% higher for the BMI loss group than for the BMI stable group (RR = 1.65, P < .001) and 53% higher for the BMI gain group than for the BMI stable group (RR = 1.53, P = .001). Age moderated the BMI change differences. That is, the higher mortality risks associated with BMI loss and gain were less severe in very old age.
Old persons who were overweight had a lower mortality risk than old persons who were of normal weight, even after controlling for weight change and multimorbidity. Persons who increased or decreased in BMI had a greater mortality risk than those who had a stable BMI, particularly those aged 70 to 79. This study lends further support to the belief that the World Health Organization guidelines for BMI are overly restrictive in old age.
Objectives:
To identify predictors and consequences of nutritional risk, as determined by the Mini Nutritional Assessment (MNA), in older recipients of domiciliary care services living at home.
Design:
Baseline analysis of subject characteristics with low MNA scores (<24) and follow-up of the consequences of these low scores.
Setting:
South Australia.
Participants:
Two hundred fifty domiciliary care clients (aged 67-99, 173 women).
Measurements:
Baseline history and nutritional status were determined. Information about hospitalization was obtained at follow-up 12 months later.
Intervention:
Letters suggesting nutritional intervention were sent to general practitioners of subjects not well nourished.
Results:
At baseline, 56.8% were well nourished, 38.4% were at risk of malnutrition, and 4.8% were malnourished (43.2% not well nourished). Independent predictors of low MNA scores (<24) were living alone, and the physical and mental component scales of the 36-item Short Form Health Survey. Follow-up information was obtained for 240 subjects (96%). In the ensuing year not well-nourished subjects were more likely than well-nourished subjects to have been admitted to the hospital (risk ratio (RR) = 1.51, 95% confidence interval (CI) = 1.07-2.14), have two or more emergency hospital admissions (RR = 2.96, 95% CI = 1.15-7.59), spend more than 4 weeks in the hospital (RR = 3.22, 95% CI = 1.29-8.07), fall (RR = 1.65, 95% CI = 1.13-2.41), and report weight loss (RR = 2.63, 95% CI = 1.67-4.15).
Conclusion:
The MNA identified a large number of subjects with impaired nutrition who did significantly worse than well-nourished subjects during the following year. Studies are needed to determine whether nutritional or other interventions in people with low MNA scores can improve clinical outcomes.
Objective:
To determine the excess mortality associated with obesity (defined by body mass index (BMI)) in older people, with and without adjustment for other risk factors associated with mortality and for demographic factors.
Design:
Retrospective cohort analysis of the Longitudinal Study of Aging (LSOA).
Setting:
Nationally representative sample of community-dwelling older people.
Participants:
Seven thousand five hundred and twenty-seven participants age 70 and older in 1984.
Measurements:
We used Cox regression to calculate proportional hazards ratios for mortality over 96 months. We tested the hypothesis that increased BMI (top 15%) increased mortality rates in older people.
Results:
Death occurred in 38% of the cohort: 54% of the thin (lowest 10% of the population, BMI <19.4 kg/m(2)), 33% of the obese (highest 15%, BMI> 28.5 kg/m(2)), and 37% of the remaining participants (normal) died. Adjustment for demographic factors, health services utilization, and functional status still demonstrated reduced mortality in obese older people (hazard ratio 0.86, 95% confidence interval (CI) = 0.77-0.97) compared with normal. After adjustment, thin older people remained more likely to die (hazard ratio 1.46, 95% CI = 1.30-1.64) than normal older people. Sensitivity analyses for income, mortality during the first two years of follow-up, and medical comorbidities did not substantively alter the conclusions.
Conclusion:
Obesity may be protective compared with thinness or normal weight in older community-dwelling Americans.
OBJECTIVES: Older people are at risk of undernutrition because of a number of physiological conditions and lifestyle factors. The purpose of this study was to explore the predictive relationship of corrected arm muscle area (CAMA) with 8-year mortality in a representative sample of older Australians.
DESIGN: Prospective cohort study: The Australian Longitudinal Study of Ageing.
SETTING: Community.
PARTICIPANTS: One thousand three hundred ninety-six participants aged 70 and older.
MEASUREMENTS: Trained observers measured baseline weight, height, mid upper arm circumference, and triceps skinfold thickness using standard techniques. Body mass index (BMI) and CAMA were calculated. Baseline BMI and CAMA measurements were categorized according to cutoff values proposed by Garrow et al. and Friedman et al., respectively. Subsequent analyses were undertaken using Cox proportional hazards regression.
RESULTS: After adjustment for potential confounders (baseline age, gender, marital status, smoking, self-rated health, ability to conduct activities of daily living, comorbidity, cognition performance, and presence of depression), those older Australians with a low CAMA (≤21.4 cm2 for men and ≤21.6 cm2 for women) had an increased risk of mortality at 8-year follow-up (hazard ratio = 1.94, 95% confidence interval = 1.25–3.00, P = .003). There was no increased risk in 8-year mortality identified for those with a high or low BMI.
CONCLUSION: CAMA is a useful assessment of undernutrition in older adults that has better prognostic value than BMI in predicting death in older, community-living Australians.
Background
Physical performance may predict survival independently of other current predictors in non selected elderly subjects. We determined if poor balance and decreased gait speed may predict mortality after adjustment for both baseline and follow-up confounders in well-functioning elderly women.
Methods
A subgroup of participants in the Epidemiology of osteoporosis (EPIDOS) study (N = 1,300) was followed for 8 years. Participants were community-dwelling women aged 75 or older able to go outside home without assistance. The baseline examination included a questionnaire and a clinical and functional examination. Participants were contacted every year thereafter by mail.
Results
Poor balance, defined by the inability to stand in a tandem position or to complete ten foot taps in less than 4.6 seconds, and poor mobility, defined by a gait speed of less than 0.80 m/s or a stride length of less than 0.5 m were significant predictors of low 8-year survival, independently of other predictors of death at baseline (educational level, social network, number of drugs, fear of falling, visual acuity, perceived health, IADL score, physical activity, and comorbidities) and during follow-up (falls, IADL score, the need to be accompanied to go outside, weight loss, hospitalization, and the report of new comorbidities).
Conclusion
The current study shows that poor balance and mobility are significant predictors of 8-year mortality independently of baseline and intermediate events in pre-disabled women aged 75 years and older, suggesting that they may reflect a certain failure to respond adequately in the face of present and future medical and non-medical events.
A WHO expert consultation addressed the debate about interpretation of recommended body-mass index (BMI) cut-off points for determining overweight and obesity in Asian populations, and considered whether population-specific cut-off points for BMI are necessary. They reviewed scientific evidence that suggests that Asian populations have different associations between BMI, percentage of body fat, and health risks than do European populations. The consultation concluded that the proportion of Asian people with a high risk of type 2 diabetes and cardiovascular disease is substantial at BMIs lower than the existing WHO cut-off point for overweight (⩾25 kg/m2). However, available data do not necessarily indicate a clear BMI cut-off point for all Asians for overweight or obesity. The cut-off point for observed risk varies from 22kg/m2 to 25kg/m2 in different Asian populations; for high risk it varies from 26kg/m2 to 31kg/m2. No attempt was made, therefore, to redefine cut-off points for each population separately. The consultation also agreed that the WHO BMI cut-off points should be retained as international classifications. The consultation identified further potential public health action points (23·0, 27·5, 32·5, and 37·5 kg/m2) along the continuum of BMI, and proposed methods by which countries could make decisions about the definitions of increased risk for their population.
To assess the association between Body Mass Index (BMI) and cause-specific mortality in older adults and to assess which BMI was associated with lowest mortality.
Prospective study.
European towns.
1,980 older adults, aged 70-75 years from the SENECA (Survey in Europe on Nutrition and the Elderly: a concerted action) study.
BMI, examined in 1988/1989, and mortality rates and causes of death during 10 years of follow-up.
Cox proportional hazards model including both BMI and BMI², accounting for sex, smoking status, educational level and age at baseline showed that BMI was associated with all-cause mortality (p<0.01), cardiovascular mortality (p<0.01) and mortality from other causes (p<0.01), but not with cancer or respiratory mortality (p>0.3). The lowest all-cause mortality risk was found at 27.1 (95%CI 24.1, 29.3) kg/m², and this risk was increased with statistical significance when higher than 31.4 kg/m² and lower than 21.1 kg/m². The lowest cardiovascular mortality risk was found at 25.6 (95%CI 17.1, 28.4) kg/m², and was increased with statistical significance when higher than 30.9 kg/m².
In this study, BMI was associated with all-cause mortality risk in older people. This risk was mostly driven by an increased cardiovascular mortality risk, as no association was found for mortality risk from cancer or respiratory disease. Our results indicate that the WHO cut-off point of 25 kg/m² for overweight might be too low in old age, but more studies are needed to define specific cut-off points.
Elderly subjects are at risk for undernutrition. Restrictive diets may increase this risk. The aim was to evaluate the impact of restrictive diets on undernutrition and its risk in free-living elderly.
Ambulatory patients over age 75 and under a restrictive diet (low salt, low cholesterol, diabetic) were included prospectively, along with age- and gender-matched controls. Weight and height were measured, and the short-form of the Mini Nutritional Assessment was scored. Groups were compared to determine variables associated with a low MNA-SF(®).
95 patients in the diet group (62 F, 33 M, 80 ± 4 y) and 95 controls (57 F, 38 M, 82 ± 5 y) were included. Restrictive diets (low salt n = 33, diabetic n = 19, low cholesterol n = 15, combination n = 27) had been followed since 11.0 ± 5.9 years. Using the cut-off of 12 for MNA-SF(®), 44 patients in the diet group were at risk vs. 22 among controls (P < 0.001). In multivariate analysis, a restrictive diet increased the probability of having an MNA-SF(®) < 12 (OR = 3.6, (95%)CI = 1.8-7.2, P < .001).
Restrictive diets in patients over 75 increase the risk of undernutrition. On an individual level, these diets may need reassessment. Society guidelines should promote specific recommendations for the elderly.