Enhanced Cardiovascular Risk and Altered Oxidative Status in Elders with Moderate Excessive Body Fat

Abstract

Aging and obesity are linked to oxidative stress. Oxidative stress may mediate age-related cardiovascular diseases. Although the body mass index (kg/m²) defines obesity (≥30) and overweight (25–29.9), it may fail to detect crucial differences in body fat content in elders. Consequently, we measured body fatness in 42 healthy elders and evaluated their cardiovascular risk factors and the extent of their physical activity. We assessed plasma, erythrocytes, and saliva oxidative stress biomarkers in this population. A higher fat mass was associated with a less active lifestyle, more metabolic syndrome components, an enhanced Framingham 10-year risk score, and augmented insulin resistance. Individuals with excessive body fat had significantly less oral peroxidase enzymes activity than those with normal body fat. Erythrocyte susceptibility to oxidative hemolysis, previously reported to be elevated with physical activity, was marginally lower in the higher fat group. Other biomarkers of oxidative stress in saliva, plasma, and erythrocytes were similar in both groups. A 6% elevation in body fat with a less active lifestyle and an increased cardiovascular risk is associated with a decline in salivary anti-oxidative activity. Such reduced activity may contribute to deteriorating oral health in obese elders. Thus, this study provides novel information on the contribution of excessive body fat to oxidative status and cardiovascular risk in old age.

Introduction

According to the oxidative stress (OS) theory, an imbalance between pro-oxidants and anti-oxidants leads to oxidative damage. Oxidative damage to cellular macromolecules increases with age and contributes to a progressive decline in cell function and to an accelerated aging process.¹ A recent review suggests that OS has only a limited effect on life span but may affect several features of the aging process, especially in individuals under chronic stress conditions.² There is conflicting evidence whether deterioration of anti-oxidant mechanisms plays a role in oxidative damage in aging. However, the role of increased reactive oxygen species (ROS) production in aging is less controversial.^3
–5

Several OS-related diseases, such as cancer, diabetes mellitus, neurodegeneration, atherosclerosis, and cardiovascular disease, are more common among elders. Similarly, overweight and obesity increase the risk of such morbidities.^6

–9 A common denominator for aging and obesity may be linked to elevated OS. Obesity results in excessive chronic inflammation and production of ROS emerging from active mediators, chemotactic molecules, and adipokines.¹⁰

Despite the strong association between OS and disease states, a direct connection between both is not a universal phenomenon. For instance, lifetime training, which contributes to life quality and survival and is therefore recommended for reducing the risk of many diseases, is associated with an elevation in OS biomarkers.^11
–13 Moreover, anti-oxidant therapy is still an appealing strategy for the prevention and treatment of various OS-related morbidities, including cardiovascular disease.

In the plasma, common measured OS markers include protein carbonyls and lipid peroxides.^14,15 At the cellular level, erythrocytes can be used to study oxidative defense systems. They possess potent anti-oxidant activity consisting of both enzymatic (such as catalase) and non-enzymatic pathways.¹⁶ A general indicator of both anti-oxidant pathways is the erythrocytes oxidative hemolysis test. When an oxidant is added to the cells ex vivo, the extent of hemolysis can be measured, reflecting the membrane anti-oxidant coping abilities.¹⁷

Blood and saliva oxidation states are positively correlated.^18,19 The examination of saliva in addition to blood may provide a global picture of the body's oxidative status. The anti-oxidant system efficiency of the saliva decreases with age, as does its total antioxidant capacity (TAC).²⁰ Although uric acid is saliva's dominant anti-oxidant molecule, oral peroxidases (OPO) are the main anti-oxidant enzymes. The OPO are composed of two enzymes with different origins. Salivary peroxidase is secreted mainly from the parotid gland, and myeloperoxidase is secreted from leukocytes migrating to the oral cavity.^21,22 In addition to hydrogen peroxide (H₂O₂) peroxidation, OPO serves as an anti-microbial, an anti-mutagenic, and an anti-carcinogenic, and its activity is important in preserving oral health.²³ These enzymes are essential for oral enzymatic OS coping systems.

In a recent meta-analysis of all-cause mortalities reported in the Journal of the American Medical Association, in about 2.9 million individuals, including elderly people, overweight [30> body mass index (BMI)≥25] was associated with lower all-cause mortality rates compared to normal weight (24.9>BMI≥18.5) and obesity (BMI≥30).²⁴ Hence, this population comprises a special interest group among the elderly population.

Most of the studies describing the oxidative status in elders with different body compositions compare normal weight with obese subjects. Although BMI is widely used, it does not accurately measure fat content and it may fail to detect crucial differences in body fat and skeletal muscle content.²⁵ BMI guidelines rely on the assumption that it is highly correlated with body fatness and consequent morbidity and mortality. However, BMI is also correlated with lean body mass, and it poorly represents body fatness in some individuals. Thus, measuring BMI may lead to body-composition misclassification.²⁶ Therefore, in this study, body fat percentage was used for proper classification of body composition.

The purpose of this study was to investigate oxidative modifications of plasma macromolecules and the anti-oxidant defense systems in erythrocytes and saliva in relation to cardiovascular risk factors in excessive body fat (EBF) versus normal body fat (NBF) of free-living elderly subjects. The study of oxidative status biomarkers in different body compartments among subjects with an average 6% difference in body fat enables us to explore the systemic oxidative effects of moderate excessive fat in the elderly population.

Methods

Subjects

This cross-sectional study included 42 healthy volunteers (16 men and 26 women) aged 60–78 years in the final analysis recruited from the Beit HaShita Kibbutz community under the care of a local family clinic in northern Israel. The subjects were divided into two groups. The EBF group comprised 11 men and 18 women with body fat percentages higher than the threshold of 25% and 36%, respectively, according to gender. The NBF group consisted of five men and eight women with body fat percentages lower than the mentioned threshold. The body fat definition was based on the Gallagher et al. approach for developing healthy body fat percentage range guidelines based on BMI.²⁶ Inclusion criteria included participants older than 60 years. Participants were excluded if they had unbalanced diabetes, neurodegenerative diseases, active infection, cancer, inflammatory bowel disease, kidney disease, or liver disease or if they were current smokers (more than two cigarettes per day). This study addressed and included all of the Kibbutz candidates who were willing to volunteer and fulfilled the inclusion criteria.

Venous blood and saliva samples, as well as information on anthropometric measurements and physical activity lifestyle, were collected from each subject in the morning after 12 hr of fasting. The study was approved by the Institutional Helsinki Committee at Rambam Health Care Campus, Haifa, Israel. All participants provided a written informed consent.

Anthropometric, physical activity, and cardiovascular risk assessment

Body mass and body fat percentage were measured with the bioelectrical impedance (BIA) balance Tanita BC-545 (Tanita Corporation of America Inc., Arlington Heights, IL) to the nearest 0.1 kg and 0.1%, respectively. BIA is a valid method for estimating body composition in the elderly population as compared to dual-energy X-ray absorptiometry (DXA), which is currently the “gold standard” method.²⁷ Fat mass was calculated by multiplying weight with body fat percentage. Waist and hip circumferences were measured by a single qualified person using a tape measure at the superior iliac crest and at the widest location of the hips while the subject was standing in a relaxed posture. Physical activity was assessed using the Physical Activity Scale for the Elderly (PASE).²⁸ The PASE score consists of questions on frequency and levels of exertion in recreational sport and leisure, home, and work activities over a 1-week recall period. Metabolic syndrome was defined according to the International Diabetes Federation (IDF) criteria. Components of the metabolic syndrome include central obesity (≥94 cm and ≥80 cm for men and women, respectively), raised triglycerides (≥150 mg/dL, or specific treatment for this lipid abnormality), blood pressure (≥130 mm/Hg and ≥85 mm/Hg for systolic [SBP] and diastolic blood pressure [DBP], respectively, or treatment of previously diagnosed hypertension), fasting plasma glucose (≥100 mg/dL) and reduced high-density lipoprotein cholesterol (HDL-C) (<40 mg/dL and <50 mg/dL for men and women, respectively, or specific treatment for this lipid abnormality).²⁹ The Framingham risk score for coronary heart disease over 10 years was calculated according to the method presented in Anderson et al.³⁰ Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as follows: Fasting insulin (mU/L)×fasting glucose (mmol/L)/22.5.³¹

Blood analysis

Blood samples underwent analysis of glucose, insulin, lipid profile, and hemoglobin immediately after drawing using automated diagnostic equipment (Abbott Architect Instruments, Abbott Park, Chicago, IL). For the remaining assays, blood was collected into disposable vials containing EDTA and separated for plasma and erythrocytes. Plasma was stored at −80°C for subsequent analyses of oxidized proteins and lipids. The plasma protein carbonyl assay was performed according to Reznick and Packer's procedure and is presented in nmol/mg of plasma proteins.³² The plasma lipid peroxidation assays, thiobarbituric acid reactive substances (TBARS), and plasma peroxides are described elsewhere and are expressed as nmol/mL of plasma.³³

Erythrocytes were washed three times with isotonic saline and kept for 6 days at 4°C for the catalase assay and analyzed immediately for the hemolysis test (HT) induced by 2,2′-azo-bis(2-amidinopropane) dihydrochloride (AAPH). The test was based on previously reported protocols^34,35 and modified as follows: 0.4 mL of washed erythrocytes were added to 9.5 mL of saline. Two milliliters of diluted erythrocytes were added to double-distilled water (DDW), 100 mM AAPH, or saline to a final tube volume of 4 mL. All tubes were incubated at 37°C for 2.5 hr with intermittent shaking. After centrifuging the incubated tubes at 4000 rpm for 10 min, absorbance was read spectrophotometrically at 540 nm. The HT percentage was calculated by dividing the absorbance of the AAPH-induced hemolysis by the absorbance of the DDW-induced hemolysis. Saline tubes served as a negative control. Catalase activity was determined in erythrocyte hemolysates according to Aebi's method³⁶ and expressed as mU/mg of hemoglobin protein.

Saliva analysis

Un-stimulated whole saliva was collected into 50-mL falcon tubes and stored at 4°C until sedimentation of the squamous cells and cell debris. The supernatant was used for the following assays. The saliva's hydrophilic and lipophilic anti-oxidant capacity was measured by the TAC kit (ScienCell Research Laboratories, CA) at 540 nm and expressed as mM Trolox equivalents per mg of salivary supernatant protein. The activity of OPO was measured according to the 5-thio-2-nitrobenzoic acid thiocyanate (NBS-SCN) assay. Briefly, in this assay OSCN-hypothiocyanite, the product of OPO enzymes, oxidizes NBS to 5,5′-dithiobis, 2-nitrobenzoic acid (DTNB) and its formation is monitored at 412 nm. One unit of enzyme activity was defined as the level of enzyme activity needed to cleave 1 mmol of NBS/min at 22°C, using a molar extinction coefficient of 12,800. The OPO enzymes activity is expressed as mU/mg of salivary supernatant protein.²¹

Statistical analysis

All results are expressed as mean±standard error (SE). The Student t-test was used for statistical analysis of different body fat groups' anthropometry. The variables were checked for normal distribution using the Shapiro–Wilk test. Differences in physical activity, cardiovascular risk factors, and oxidation status biomarkers were processed by analysis of co-variance (ANCOVA) and adjusted for age and gender. The Framingham 10-year risk scores were transformed into logarithmic units to prevent homogeneity violation. The assumptions for ANCOVA were met. The Pearson chi-squared test was used to compare the proportions of the metabolic syndrome subjects between groups. Partial correlation coefficients (Pearson r) were calculated to assess the relationship between BMI and body fat percentage (controlled for gender) and between fat mass (controlled for age and gender) or age (controlled for gender and fat percentage) with oxidation status biomarkers, PASE scores, or cardiovascular risk factors. All statistical tests were two-tailed with the significance level set at a level of 0.05. SPSS Statistics 17 (SPSS Software, Chicago, IL) was used for statistical analysis.

Results

BMI was positively and significantly correlated with body fat percentage (r=0.732, p=5×10⁻⁸) and fat mass (r=0.836, p=1×10⁻¹¹). Table 1 describes the anthropometric parameters of the two different body fat groups. Weight, BMI, body fat (%), fat mass, and circumferences were significantly higher in the EBF group. Age, height, and waist-to-hip ratios did not significantly differ between groups.

Table 1.

Comparison of Values for Anthropometry of Different Body Fat Groups

Variable	NBF (n=13)	EBF (n=29)	p value
Age (yeasrs)	65.3±1.1	68.5±1.0	0.059
Height (cm)	162±2	164±2	0.553
Weight (kg)	71.2±3.3	80.4±2.3	0.028^*
BMI (kg/m²)	26.8±0.6	29.7±0.6	0.004^*
Body fat (%)	30.0±1.5	35.7±1.1	0.006^*
Fat mass (kg)	20.8±0.6	28.4±0.9	2×10^–8 ^*
Waist circumference (cm)	92.5±3.0	100.5±1.6	0.015^*
Hip circumference (cm)	102±1	108.3±1.1	0.002^*
WHR	0.90±0.02	0.93±0.01	0.340

Values are mean±standard error (SE), p values were calculated by the Student t-test.

p value<0.05. Men and women subjects' ratio was equal among groups, hence the values are presented without gender classification.

NBF, normal body fat; EBF, excessive body fat; BMI, body mass index; WHR, waist-to-hip ratio.

Table 2 shows the physical activity and cardiovascular risk parameters of the two groups. The PASE questionnaire revealed that subjects with higher fat were less physically active. The Framingham risk score for coronary heart disease took into account factors such as total cholesterol, HDL-C, and SBP. The total risk score as well as the SBP were higher, with a marginal significance in the EBF group compared to the HBF group. Although fasting glucose was not higher among the EBF group, insulin levels and resistance as demonstrated by HOMA-IR were significantly higher. In addition, EBF subjects had a higher tendency of metabolic syndrome (55% in the EBF group vs. 31% in the NBF group, p=0.143) (Fig. 1). Thus, the EBF group was less physically active with a higher risk of cardiovascular disease and other obesity related co-morbidities.

FIG. 1.

Metabolic syndrome components distribution among groups. Values are a percentage of subjects with zero to five metabolic syndrome components (according to IDF criteria). IDF, International Diabetes Federation; NBF, normal body fat; EBF, excessive body fat.

Table 2.

Exercise and Cardiovascular Risk Factors Characteristics

Variable	NBF	EBF	p value
PASE score	219±18	171±12	0.035^*
Framingham risk score (logarithmic units)	0.68±0.07	0.84±0.05	0.077
Triglycerides (mg/dL)	110±19	149±12	0.100
Total cholesterol (mg/dL)	208±12	200±8	0.610
HDL-C (mg/dL)	53.7±3.5	54.4±2.3	0.868
SBP (mmHg)	127±7	142±4	0.088
DBP, (mmHg)	81.0±4.1	85.8±2.6	0.338
HOMA-IR	1.91±0.29	2.72±0.19	0.03^*
Glucose (mg/dL)	93.9±2.8	97. 7±1.8	0.275
Insulin (pmol/L)	57.1±7.0	77.1±4.6	0.026^*

Values are mean±standard error (SE) adjusted to age and gender, p values were calculated by analysis of co-variance (ANCOVA).

p value<0.05.

NBF, normal body fat; EBF, excessive body fat; PASE, Physical Activity Scale for the Elderly; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; HOMA-IR, homeostatic model assessment of insulin resistance.

Table 3 shows OS biomarkers and defense systems in the two body fat groups. Plasma markers of protein and lipid oxidative modifications showed no significant differences between the body fat groups. An examination of the erythrocyte anti-oxidant defense systems showed no difference in the catalase activity between groups, although a trend in the EBF group toward better resistance to oxidative hemolysis of the erythrocytes was revealed in the HT results.

Table 3.

Oxidative Stress Biomarkers and Oxidative Defense Systems in Different Compartments

Compartment	Variable	NBF	EBF	p value
Plasma	Protein carbonyls/protein (nmol/mg)	0.60±0.08	0.72±0.05	0.210
	TBARS (nmol/mL)	11.4±0.7	11.0±0.4	0.555
	Peroxides (nmol/mL)	470±16	475±9	0.800
Erythrocytes	HT (%)	73.0±3.7	64.3±2.4	0.065
	Catalase activity/hemoglobin (mU/mg)	32.0±1.1	34.1±0.7	0.117
Saliva	TAC (Trolox equivalents)/protein (mM/mg)	4.54±1.12	5.91±0.78	0.345
	OPO activity/protein (mU/mg)	332±32	208±17	0.003^*

Values are mean±standard error (SE) adjusted to age and gender; p values were calculated by analysis of co-variance (ANCOVA).

p value<0.05.

NBF, normal body fat; EBF, excessive body fat; TBARS, thiobarbituric acid reactive substance; HT, erythrocytes AAPH-induced hemolysis test; TAC, total anti-oxidant capacity; OPO, oral peroxidases.

No difference was found in the salivary anti-oxidant systems measured between groups. Nonetheless, the saliva major anti-oxidant enzymes, OPO, were significantly less active in the EBF group. Adjustment to PASE scores in addition to age and gender did not influence the statistical power of the difference between the groups regarding OPO activity (p=0.007). However, there was no more significant trend in the difference between the groups HT findings in the erythrocytes (p=0.103). Physical activity level was significantly and inversely correlated with fat mass (r=−0.339, p=0.032), in contrast to significant positive correlations of fat mass with metabolic syndrome components (r=0.314, p=0.048), the logarithmic Framingham risk scores (r=0.355, p=0.029), and HOMA-IR (r=0.442, p=0.004).

Analysis of the subjects regardless of fat percentage revealed only a significant inverse correlation of age with OPO activity (r=−0.426, p=0.014). Other OS biomarkers did not show any decline in the anti-oxidant system or oxidative modification with age.

Discussion

This study was designed to investigate the effects of moderate excessive body fat mass on cardiovascular risk factors, the anti-oxidative systems of erythrocytes and saliva, and OS biomarkers in the plasma in elders. Due to the criticism concerning BMI as a reliable predictor of body composition, our subjects were divided into two groups according to a healthy body fat percentage threshold.²⁶ BMI correlations with body fat percentage and mass were high. An average of 6% body fat and 3 BMI unit differences were accompanied by a 22% lower PASE score in the EBF group. In a study on Mexican Americans aged 75 years, higher physical activity scores had an important clinical relevance. This study showed that active elders with higher PASE scores had a reduced 3-year risk hazard ratio of mortality of about 0.5 compared to sedentary subjects.³⁷ Additionally, our EBF group was characterized by a tendency toward the metabolic syndrome, higher Framingham 10-year risk for coronary heart disease, and higher insulin resistance. Individuals with healthy body fat that exceeded the threshold by only several percentages had significant clinical implications. Thus, elevated risk factors in the EBF group justify the investigation into their OS status that may increase morbidity specifically in older ages.

After adjustment for confounders such as age and gender, our results showed that moderate excessive body fat in elders was associated with a trend toward a decrease in oxidative hemolysis, which disappeared after adjusting physical activity. In contrast, saliva anti-oxidant enzymes in the EBF group were less active (Table 3). Additionally, fat mass was also inversely correlated with PASE scores and positively with cardiovascular risk. Among OS parameters, only salivary anti-oxidant enzyme activity was associated with decreased age.

Adipose tissue, which is abundant in persons with EBF, produces adipokines, including pro-inflammatory adipokines such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). These adipokines induce the production of ROS and result in OS. Therefore, systemic OS is considered to be an independent by-product of growing adipose tissue.³⁸ Oxidative modification that gives rise to carbonyl groups generally causes a loss of function in the affected proteins. Oxidatively modified proteins observed during aging and in some human diseases might have serious deleterious effects on cellular and organ function.³⁹ Evidence has shown that plasma protein carbonyls are higher in older men and in grade I obese men compared to young men and normal weight men.⁴⁰ Furthermore, in a study of morbid obese patients before and 6 months after laparoscopic gastric banding, the plasma protein carbonyls were higher than in controls at baseline and decreased with weight loss.⁴¹ Likewise, we have previously reported that 12 weeks of vitamin E supplementation with green tea drinking resulted in a decrease in plasma protein carbonyl levels in elderly subjects.⁴² Evidently, in the current study, the extent of fatness and the number of subjects was not sufficient to demonstrate significant differences, although a similar trend was observed.

Products of plasma lipid oxidation were found to be higher in aging individuals and correlated positively with arthrosclerosis and with cognitive impairments in elders.⁴³ Similar to carbonyls, hydroperoxides were reported as higher in older and obese men.³⁹ In addition, a positive correlation between plasma TBARS and BMI was described among Japanese middle-aged individuals with BMI values ranging from normal to morbid obesity.⁴⁴ As reviewed by Vincent and Taylor, many other studies documented an elevation of plasma lipid peroxidation markers in obese versus non-obese individuals, suggesting that obesity may enhance OS.¹⁰ In our study, more than half of the subjects in the EBF group were not obese according to World Health Organization (WHO) BMI definitions.⁴⁵ Thus, similar levels of plasma lipid peroxidation markers between the two body fat groups were not surprising.

Increased susceptibility to oxidative hemolysis is present in some morbidities, such as chronic renal failure and acute hypoxia injury, and among elders, particularly in severely dependent elderly populations.^46
–48 Overweight and obesity in young women was followed by early onset of oxidative hemolysis.⁴⁹ Although elevated levels of OS biomarkers are typically regarded as deleterious, the literature is inconclusive. De Gonzalo-Calvo et al. investigated blood OS biomarkers in long-term trained elders compared to sedentary men and found higher levels of plasma and erythrocyte lipid peroxides and protein carbonyls. Despite higher OS biomarker levels, trained men were healthier and had lower medication intakes.¹³

Intravascular hemolysis is known to occur during and after physical activity.^50,51 One of the proposed mechanisms is an exercise-induced OS. Similar to higher plasma and erythrocyte lipid peroxides and protein carbonyl levels, exercise also increases erythrocyte susceptibility to oxidative hemolysis.^50,52 In our study, a lower oxidative hemolysis trend was observed in the EBF group; however, this trend disappeared with the adjustment of physical activity. Therefore, it is suggested that subjects in the EBF group had lower oxidative damage in the erythrocyte membrane prior to the HT due to reduced daily exercise. Lower baseline OS led to lesser oxidative hemolysis. It appears that the HT results in our study were not related to the activity of the erythrocyte peroxidase enzyme catalase, because no difference was found between the body fat groups. Interestingly, catalase activity was described to decrease with age, but its relation to obesity has been inconsistent, as studies describe conflicting results about the effect of excessive body weight on catalase activity.^40,53,54

Salivary TAC decreased with age,²⁰ while its relation to obesity has not been previously described. According to our results, there was no difference between the fat groups regarding salivary TAC. Still, the EBF group had 37% less OPO activity. Obesity, metabolic syndrome, and insulin resistance are related to sympathetic nervous system activation.⁵⁵ It is known that parotid gland secretions (which include salivary peroxidase) are induced by parasympathetic activation.⁵⁶ Thus, the lower OPO activation among the EBF group and high fat mass subjects might be the result of greater sympathetic nervous system activation. Measuring enzymatic activity or the concentration of each one of the two OPO enzymes is interesting and could have confirmed our proposed mechanism of sympathetic activation. Even though the OPO activity did not influence the TAC, it has an important implication for oral health because the salivary OPO system is anti-bacterial and can inhibit pathogenic and periodontopathic bacteria.⁵⁷ Because obesity has been found to be associated with increased prevalence of periodontal disease,^58,59 there is a possibility that elevated prevalence of periodontal disease among obese individuals is related to decreased OPO activity induced by excessive body fat. Thus, decreased OPO activity with advanced age further emphasizes the potential deleterious oral health effects of obesity in elders.

The limitations of this study include the relatively small cohort size. More subjects would be desirable for strengthening some marginal significant values. Participation of more obese elderly would have enabled us to compare them with moderate excessive body fat. Moreover, absence of subjects under the age of 60 years limits our conclusions regarding the elders. In addition, there was no monitoring of ROS generation in different compartments that would have also allowed more comprehensive understanding of the fat-related OS. The use of immunological or chromatographic methods for the measurement and quantification of plasma protein carbonyls and lipid peroxides is preferable, because the colorimetric method is less accurate and more prone to unspecific overestimation.^15,60 Also, examining other erythrocyte enzymes in addition to plasma OS biomarkers would have allowed a better understanding of the observed HT. Examination of the subjects' periodontal oral health could have confirmed the relationship of OPO to periodontal disease with excessive fat. Finally, the physical activity information was obtained from questionnaires that were subjected to recall bias and assessed general physical activity and not particularly high-intensity exercise that is highly correlated with ROS formation.

In conclusion, excessive body fat of 6% is associated with increased insulin resistance and elevated cardiovascular risk. Such a moderate fat accumulation combined with physical inactivity has significant effects on oxidative status indicated by the decrease in the activity of the OPO, the predominant enzymatic anti-oxidant system in the oral cavity. Previous reports have shown higher plasma lipid peroxidation in obese elders. In our study, which included participants with normal and moderate excessive body fat, the EBF group did not demonstrate higher lipid peroxidation biomarkers. The effects of lifestyle intervention on moderate excessive body fat, oxidative status, and longevity in the elderly population require further investigation.

Footnotes

Acknowledgments

We thank the Kibbutz medical staff, Yoke Roded, Smadar Lustgarten, and Hannah Shulami, for their outstanding assistance. This work was supported by the Rappaport Institute for Research; the Krol Foundation of Barnegat, NJ; Research and Scholarships Fund in Food and Nutrition Fields with Public Health Implication; and the Myers–JDC Brookdale Institute of Gerontology and Human Development and Eshel–the Association for the Planning and Development of Services for the Aged in Israel.

Author Disclosure Statement

No competing financial interests exist.

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