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Background: Body composition changes with aging lead to increased adiposity and decreased muscle mass, making the diagnosis of obesity challenging. Conventional anthropometry, including body mass index (BMI), while easy to use clinically may misrepresent adiposity. We determined the diagnostic accuracy of BMI using dual-energy X-ray absorptiometry (DEXA) in assessing the degree of obesity in older adults. Methods: The National Health and Nutrition Examination Surveys 1999–2004 were used to identify adults aged ⩾60 years with DEXA measures. They were categorized (yes/no) as having elevated body fat by gender (men: ⩾25%; women ⩾35%) and by BMI ⩾25 and ⩾30 kg m − 2 . The diagnostic performance of BMI was assessed. Metabolic characteristics were compared in discordant cases of BMI/body fat. Weighting and analyses were performed per NHANES (National Health and Nutrition Examination Survey) guidelines. Results: We identified 4984 subjects (men: 2453; women: 2531). Mean BMI and % body fat was 28.0 kg m −2 and 30.8% in men, and 28.5 kg m − 2 and 42.1% in women. A BMI ⩾30 kg m − 2 had a low sensitivity and moderately high specificity (men: 32.9 and 80.8%, concordance index 0.66; women: 38.5 and 78.5%, concordance 0.69) correctly classifying 41.0 and 45.1% of obese subjects. A BMI ⩾25 kg m −2 had a moderately high sensitivity and specificity (men: 80.7 and 99.6%, concordance 0.81; women: 76.9 and 98.8%, concordance 0.84) correctly classifying 80.8 and 78.5% of obese subjects. In subjects with BMI <30 kg m − 2 , body fat was considered elevated in 67.1% and 61.5% of men and women, respectively. For a BMI ⩾30 kg m − 2 , sensitivity drops from 40.3% to 14.5% and 44.5% to 23.4%, whereas specificity remains elevated (>98%), in men and women, respectively, in those 60–69.9 years to subjects aged ⩾80 years. Correct classification of obesity using a cutoff of 30 kg m − 2 drops from 48.1 to 23.9% and 49.0 to 19.6%, in men and women in these two age groups. Conclusions: Traditional measures poorly identify obesity in the elderly. In older adults, BMI may be a suboptimal marker for adiposity.

Background: Sarcopenia is defined as the loss of skeletal muscle mass and quality, which accelerates with aging and is associated with functional decline. Rising obesity prevalence has led to a high-risk group with both disorders. We assessed mortality risk associated with sarcopenia and sarcopenic obesity in elders. Methods: A subsample of 4652 subjects ⩾60 years of age was identified from the National Health and Nutrition Examination Survey III (1988–1994), a cross-sectional survey of non-institutionalized adults. National Death Index data were linked to this data set. Sarcopenia was defined using a bioelectrical impedance formula validated using magnetic resonance imaging-measured skeletal mass by Janssen et al. Cutoffs for total skeletal muscle mass adjusted for height 2 were sex-specific (men: ⩽5.75 kg/m 2 ; females ⩽10.75 kg/m 2 ). Obesity was based on % body fat (males: ⩾27%, females: ⩾38%). Modeling assessed mortality adjusting for age, sex, ethnicity (model 1), comorbidities (hypertension, diabetes, congestive heart failure, osteoporosis, cancer, coronary artery disease and arthritis), smoking, physical activity, self-reported health (model 2) and mobility limitations (model 3). Results: Mean age was 70.6±0.2 years and 57.2% were female. Median follow-up was 14.3 years (interquartile range: 12.5–16.1). Overall prevalence of sarcopenia was 35.4% in women and 75.5% in men, which increased with age. Prevalence of obesity was 60.8% in women and 54.4% in men. Sarcopenic obesity prevalence was 18.1% in women and 42.9% in men. There were 2782 (61.7%) deaths, of which 39.0% were cardiovascular. Women with sarcopenia and sarcopenic obesity had a higher mortality risk than those without sarcopenia or obesity after adjustment (model 2, hazard ratio (HR): 1.35 (1.05–1.74) and 1.29 (1.03–1.60)). After adjusting for mobility limitations (model 3), sarcopenia alone (HR: 1.32 ((1.04–1.69) but not sarcopenia with obesity (HR: 1.25 (0.99–1.58)) was associated with mortality. For men, the risk of death with sarcopenia and sarcopenic obesity was nonsignificant in both model-2 (HR: 0.98 (0.77–1.25), and HR: 0.99 (0.79–1.23)) and model 3 (HR: 0.98 (0.77–1.24) and HR: 0.98 (0.79–1.22)). Conclusions: Older women with sarcopenia have an increased all-cause mortality risk independent of obesity.

Objective: We performed a systematic review and meta-analysis of studies that assessed the performance of body mass index (BMI) to detect body adiposity. Design: Data sources were MEDLINE, EMBASE, Cochrane, Database of Systematic Reviews, Cochrane CENTRAL, Web of Science, and SCOPUS. To be included, studies must have assessed the performance of BMI to measure body adiposity, provided standard values of diagnostic performance, and used a body composition technique as the reference standard for body fat percent (BF%) measurement. We obtained pooled summary statistics for sensitivity, specificity, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR). The inconsistency statistic (I2) assessed potential heterogeneity. Results: The search strategy yielded 3341 potentially relevant abstracts, and 25 articles met our predefined inclusion criteria. These studies evaluated 32 different samples totaling 31 968 patients. Commonly used BMI cutoffs to diagnose obesity showed a pooled sensitivity to detect high adiposity of 0.50 (95% confidence interval (CI): 0.43–0.57) and a pooled specificity of 0.90 (CI: 0.86–0.94). Positive LR was 5.88 (CI: 4.24–8.15), I 2=97.8%; the negative LR was 0.43 (CI: 0.37–0.50), I 2=98.5%; and the DOR was 17.91 (CI: 12.56–25.53), I 2=91.7%. Analysis of studies that used BMI cutoffs 30 had a pooled sensitivity of 0.42 (CI: 0.31–0.43) and a pooled specificity of 0.97 (CI: 0.96–0.97). Cutoff values and regional origin of the studies can only partially explain the heterogeneity seen in pooled DOR estimates. Conclusion: Commonly used BMI cutoff values to diagnose obesity have high specificity, but low sensitivity to identify adiposity, as they fail to identify half of the people with excess BF%.

Background: Body mass index (BMI) is the most widely used measure to diagnose obesity. However, the accuracy of BMI in detecting excess body adiposity in the adult general population is largely unknown. Methods: A cross-sectional design of 13 601 subjects (age 20-79.9 years; 49% men) from the Third National Health and Nutrition Examination Survey. Bioelectrical impedance analysis was used to estimate body fat percent (BF% ). We assessed the diagnostic performance of BMI using the World Health Organization reference standard for obesity of BF% >25% in men and>35% in women. We tested the correlation between BMI and both BF% and lean mass by sex and age groups adjusted for race. Results: BMI-defined obesity (30 kg m- 2) was present in 19.1% of men and 24.7% of women, while BF% -defined obesity was present in 43.9% of men and 52.3% of women. A BMI30 had a high specificity (men=95% , 95% confidence interval (CI), 94-96 and women=99% , 95% CI, 98-100), but a poor sensitivity (men=36% , 95% CI, 35-37 and women=49% , 95% CI, 48-50) to detect BF% -defined obesity. The diagnostic performance of BMI diminished as age increased. In men, BMI had a better correlation with lean mass than with BF% , while in women BMI correlated better with BF% than with lean mass. However, in the intermediate range of BMI (25-29.9 kg m- 2), BMI failed to discriminate between BF% and lean mass in both sexes. Conclusions: The accuracy of BMI in diagnosing obesity is limited, particularly for individuals in the intermediate BMI ranges, in men and in the elderly. A BMI cutoff of30 kg m- 2 has good specificity but misses more than half of people with excess fat. These results may help to explain the unexpected better survival in overweight/mild obese patients.

Background/Objective: Adiponectin exerts beneficial effects by reducing inflammation and improving lipid metabolism and insulin sensitivity. Although the adiponectin level is lower in obese individuals, whether weight gain reduces adiponectin expression in humans is controversial. We sought to investigate the role of weight gain, and consequent changes in leptin, on altering adiponectin expression in humans. Methods/Results: Forty-four normal-weight healthy subjects were recruited (mean age 29 years; 14 women) and randomized to either gain 5% of body weight by 8 weeks of overfeeding ( n =34) or maintain weight ( n =10). Modest weight gain of 3.8±1.2 kg resulted in increased adiponectin level ( P =0.03), whereas weight maintenance resulted in no changes in adiponectin. Further, changes in adiponectin correlated positively with changes in leptin ( P =0.0085). In-vitro experiments using differentiated human white preadipocytes showed that leptin increased adiponectin mRNA and protein expression, whereas a leptin antagonist had opposite effects. To understand the role of leptin in established obesity, we compared adipose tissue samples obtained from normal-weight versus obese subjects. We noted, first, that leptin activated cellular signaling pathways and increased adiponectin mRNA in the adipose tissue from normal-weight participants, but did not do so in the adipose tissue from obese participants. Second, we noted that obese subjects had increased caveolin-1 expression, which attenuates leptin-dependent increases in adiponectin. Conclusions: Modest weight gain in healthy individuals is associated with increases in adiponectin levels, which correlate positively with changes in leptin. In vitro , leptin induces adiponectin expression, which is attenuated by increased caveolin-1 expression. In addition, the adipose tissue from obese subjects shows increased caveolin-1 expression and impaired leptin signaling. This leptin signal impairment may prevent concordant increases in adiponectin levels in obese subjects despite their high levels of leptin. Therefore, impaired leptin signaling may contribute to low adiponectin expression in obesity and may provide a target for increasing adiponectin expression, hence improving insulin sensitivity and cardio-metabolic profile in obesity.

Background/Objectives:Telomere shortening is associated with age and risk of medical comorbidity. We assessed the relationship between measures of adiposity, leukocyte telomere length, and mortality and whether it is modified by age.Subjects/Methods:Subjects with dual-energy X-ray absorptiometry measures were identified using the National Health and Nutrition Examination Survey 1999-2002. Obesity was categorized using two body fat definitions (BF1%: men [egs]25%; females [egs]35%; BF2% [egs]28% and [egs]38%, respectively), body mass index (BMI) and waist circumference (WC; men [egs]102 cm; females [egs]88 cm). Telomere length relative to standard reference DNA (T/S ratio) was assessed using quantitative PCR. Weighted multivariable regression models evaluated the association of telomere length with adiposity, both continuously and categorically (low/normal BF%, low/high WC and standard BMI categories). Differences in telomere length by age and adiposity were ascertained and subsequent models were stratified by age. Proportional hazard models assessed the risk of mortality by adiposity status. A telomere by adiposity interaction was tested in the entire cohort and by age category (<60 vs [egs]60 years; <70 vs [egs]70 years).Results:We identified 7827 subjects. Mean age was 46.1 years. Overall telomere length was 1.05±0.01 (s.e.) that differed by BF1% (low/high: 1.12±0.02 vs 1.03±0.02; P<0.001), BF2% (1.02±0.02 vs 1.11±0.02; P<0.001), BMI (underweight 1.08±0.03; normal 1.09±0.02; overweight 1.04±0.02; and obese 1.03±0.02;P<0.001) and WC (low/high 1.09±0.02 vs 1.02±0.02; P<0.001). Adjusted β-coefficients evaluating the relationship between telomere length and adiposity (measured continuously) were as follows: BF1% (β=-0.0033±0.0008; P<0.001), BF2% (-0.041±0.008; P<0.001), BMI (β=-0.025±0.0008; P=0.005) and WC (β=-0.0011±0.0004; P=0.007). High BF% (BF1%: β=-0.035±0.011; P=0.002; BF2%: β=-0.041±0.008; P<0.001) and WC (β=-0.035±0.011; P=0.008) were inversely related to telomere length (TL). Stratifying by age, high BF1% (-0.061±0.013), BF2% (-0.065±0.01), BMI-obesity (-0.07±0.015) and high WC (-0.048±0.013) were significant (all P<0.001). This association diminished with increasing age. In older participants, TL was inversely related to mortality (hazard ratio 0.36 (0.27, 0.49)), as were those classified by BF1% (0.68 (0.56, 0.81)), BF2% (0.75 (0.65, 0.80)), BMI (0.50 (0.42, 0.60)) and WC (0.72 (0.63, 0.83)). No interaction was observed between adiposity status, telomere length and mortality.Conclusions:Obesity is associated with shorter telomere length in young participants, a relationship that diminishes with increasing age. It does not moderate the relationship with mortality.

The impact of adiposity on mortality in older adults remains controversial. Some reports suggest that measures of general adiposity such as body mass index (BMI) predict better survival. We assessed the relationship between measures of adiposity and mortality in older adults. Cross-sectional analysis of a population-based sample. Non-institutionalized persons in the United States participating in the National Health and Nutrition Examination Surveys III and its linked mortality dataset. A subsample of 4,489 non-institutionalized survey participants aged >60 years with measures of body composition using bioimpedance. To account for possible residual confounding, smokers, subjects with heart failure, respiratory disease, kidney disease and cancer were excluded (n=2,920). Data from 1569 subjects were analysed. BMI, waist circumference (WC), waist-hip ratio (WHR), lean mass (LM) and % Body Fat (BF) were classified by tertiles (lowest=referent). Proportional-hazard models evaluated the association of anthropometric indices with overall and cardiovascular mortality. Mean age was 69.4years, and 265(16.9%) were >80 years. There were 717(47.6%) women and 792 deaths of which 284 [35.9%] were cardiovascular related. Elevated BMI was associated with reduced cardiovascular mortality (HR 0.53 [0.30–0.84]), and remained significant after adjusting for LM (HR 0.54 [0.31–0.93]). Elevated %BF was associated with reduced mortality from cardiovascular causes (HR 0.52 [0.29–0.91]). Low BMI was associated with higher risk of cardiovascular (HR 3.66 [1.25–10.69]) and overall death (HR 2.44 [1.22–4.90]). Measures of adiposity in older participants are associated with lower mortality from cardiovascular causes that cannot be explained by major known confounders between obesity and mortality. Further studies need to elucidate a possible protective role and interplay between adiposity and skeletal muscle in older adults.

Digital health interventions (DHI) have been shown to improve intermediates of cardiovascular health, but their impact on cardiovascular (CV) outcomes has not been fully explored. The aim of this study was to determine whether DHI administered during cardiac rehabilitation (CR) would reduce CV-related emergency department (ED) visits and rehospitalizations in patients after percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS). We randomized patients undergoing CR following ACS and PCI to standard CR (n=40) or CR+DHI (n=40) for 3 months with 3 patients withdrawing from CR prior to initiation in the treatment arm and 6 in the control group. The DHI incorporated an online and smartphone-based CR platform asking the patients to report of dietary and exercise habits throughout CR as well as educational information toward patients' healthy lifestyles. We obtained data regarding ED visits and rehospitalizations at 180 days, as well as other metrics of secondary CV prevention at baseline and 90 days. Baseline demographics were similar between the groups. The DHI+CR group had improved weight loss compared to the control group (−5.1±6.5 kg vs. −0.8±3.8 kg, respectively, P=.02). Those in the DHI+CR group also showed a non-significant reduction in CV-related rehospitalizations plus ED visits compared to the control group at 180 days (8.1% vs 26.6%; RR 0.30, 95% CI 0.08-1.10, P=.054). The current study demonstrated that complementary DHI significantly improves weight loss, and might offer a method to reduce CV-related ED visits plus rehospitalizations in patients after ACS undergoing CR. The study suggests a role for DHI as an adjunct to CR to improve secondary prevention of CV disease. This trial is registered at clinicaltrials.gov (NCT01883050).

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults ( N  = 11,573 intervention; N  = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01–1.61), P  = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08–1.91), P  = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P  = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P  < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care. In a pragmatic, cluster-randomized clinical trial, use of an AI algorithm for interpretation of electrocardiograms in primary care practices increased the frequency at which impaired heart function was diagnosed.