| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Part 4: Metabolic Syndrome and Nutrition in PD |
Division of Baxter Novum and Renal Medicine, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
Correspondence to: B. Lindholm, Division of Baxter Novum and Renal Medicine, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, K-56, Karolinska University Hospital, Huddinge, Stockholm S-141 86 Sweden. bengt.lindholm{at}ki.se
 |
ABSTRACT |
|---|
 TOP ABSTRACT DEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...CONCLUSIONSREFERENCES |
|---|
Metabolic syndrome (MetS) is defined as a cluster of risk factors for
type 2 diabetes and cardiovascular disease; it is also an independent risk
factor for developing chronic kidney disease (CKD) in the general population.
Therefore, CKD has many similarities and associations with MetS, and the
individual risk factors constituting MetS—especially insulin resistance
and glucose intolerance, hypertension, dyslipidemia, and obesity—are
also common features of the early stages of CKD. In the later stages of CKD,
uremia per se and uremic complications such as fluid retention,
protein–energy wasting, inflammation, and oxidative stress further
contribute to an increase in the prevalence of MetS in CKD patients. In
addition, PD patients exposed to glucose-based PD fluids have an increased
risk of developing metabolic complications. The broad use of MetS in clinical
research has raised the awareness of the public and of individual patients
concerning the value of lifestyle interventions. However, the definition and
pathogenesis of MetS are still debated, and no standardized definition nor
proven prognostic value has been established for MetS as a cluster of risk
factors for diabetes or cardiovascular disease in PD patients. Furthermore,
considering the paradoxical associations of some of the risk factors in MetS
with decreased mortality, another set of risk factors—those specific to
patients with uremia (for example, inflammation and malnutrition)—and
the appropriate cut-off levels to individual MetS risk factors should be taken
account at the same time. Also, the benefit of interventions targeting these
risk factors should be clarified in further clinical studies.
KEY WORDS: Metabolic syndrome; chronic kidney disease.
Metabolic syndrome (MetS) consists of a cluster of certain clinical traits with metabolic and hemodynamic alterations, including abdominal obesity, high blood pressure, insulin resistance, and dyslipidemia, which individually or in combination increase the risk for developing overt diabetes (1), cardiovascular disease (CVD) (2), chronic kidney disease (CKD) (3,4), and cardiovascular mortality (5).
The syndrome was initially described by Kylin in 1923 (6) as including hypertension, hyperglycemia, and gout; subsequently, the association between upper body (male type) obesity with cardiovascular risk was reported by Vague in 1947 (7). In 1988, the concept was expanded by Reaven, who postulated it as "syndrome X," which included insulin resistance, hyperinsulinemia, glucose intolerance, dyslipidemia [hypertriglyceridemia and decreased high-density lipoprotein (HDL) cholesterol], and hypertension (8). As the concept evolved, "metabolic syndrome" replaced the term "syndrome X" or "insulin resistance syndrome," which defines the condition with clustering of risk factors for type 2 diabetes and CVD. However, a common underlying pathologic process that results in the individual components of this syndrome has not yet been clarified, and therefore the definition and clinical usefulness of MetS have been debated.
|
DEFINITION AND CLINICAL IMPACT OF MetS IN THE GENERAL POPULATION |
|---|
|
TOPABSTRACTDEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...CONCLUSIONSREFERENCES |
|---|
|
According to the 1998 WHO definition (9), MetS was to be diagnosed by the presence of 1 marker of insulin resistance (impaired disposal of glucose under hyperinsulinemic, euglycemic clamp condition or impaired glucose tolerance, impaired fasting glucose, type 2 diabetes mellitus) associated with at least 2 of the following abnormalities: obesity, hypertension, high triglycerides, low HDL cholesterol, or microalbuminuria. The definition of insulin resistance in the WHO criteria was further modified by the European Group for the Study of Insulin Resistance, who suggested that a more practical definition of insulin resistance for clinical study would be the 25% of the population with the highest insulin resistance or the highest fasting insulin concentrations in a nondiabetic population (13).
In 2001, NCEP-ATP III proposed different criteria to define the syndrome based on the presence of 3 or more of the following abnormalities: abdominal obesity, hypertriglyceridemia, low HDL cholesterol, high blood pressure, and high fasting glucose (10). In 2005, the NCEP-ATP III definition was modified by AHA/NHLBI, leading to the threshold level of impaired fasting glucose being reduced to 100 mg/dL from 110 mg/dL.
More recently, in 2005, the IDF proposed different criteria, which regard abdominal obesity as a central component of MetS; the group therefore specifies 4 different ethnic thresholds for abdominal obesity. According to the IDF definition, MetS was to be diagnosed by the presence of abdominal obesity and 2 additional risk factors listed in NCEP-ATP III (11). That same year, AHA/NHLBI proposed a definition of MetS that used criteria similar to those of the NCEP-ATP III with some minor modifications (12).
The prevalence of MetS in earlier reports varied widely according to race or ethnic characteristics, age or sex distribution of the relevant population, and the criteria applied for diagnosis of MetS. According to the National Health and Nutrition Examination Survey (NHANES) 1999–2000 (14), the age-adjusted prevalence of MetS was 27.0%, and women had higher prevalence than men (29.0% vs 25.2%).
The prevalence of MetS increases as the age of the population increases. However, even in the same age group, the prevalence varies in urban populations from 8% in India to 24% in the United States in men. Also, the prevalence varies significantly among race and ethnic groups in the United States: 21% in non-Hispanic white women to 33% in Mexican American women of the same age. The prevalence is higher in men than women in some populations; in other populations, it is higher in women than men (15).
In an epidemiology study in Finland and Sweden, in which MetS was diagnosed by the WHO criteria, and cardiovascular mortality was assessed in 3606 subjects with a median follow-up of 6.9 years, MetS was associated with all-cause and cardiovascular mortality (16). Also, the prevalence of coronary artery disease (CAD), myocardial infarction, and stroke in the subjects with MetS was 3 times that in the other participants. Moreover, in a meta-analysis of longitudinal studies, Gami et al. reported that individuals with MetS are at increased risk of incident cardiovascular events and death (17). From 43 cohorts involving more than 170 000 individuals, subjects with MetS had a relative risk of cardiovascular events and death of 1.78 (95% confidence interval: 1.58 to 2.0). In another outcome study combining data from two prospective studies in elderly populations, MetS and its components were associated with type 2 diabetes mellitus, but only weakly or not at all associated with CVD risk, suggesting that the pattern of risk factors for new-onset diabetes differs from that for vascular events in elderly subjects (18). The same group also showed that MetS is a stronger predictor of new-onset diabetes than of CAD, and that it is an inferior predictor for CAD as compared with the Framingham risk score in the younger population (19).
These studies raise questions about the clinical usefulness of MetS as a predictor of both diabetes and CVD, and suggest that optimal risk algorithms should be developed for each disease rather than use a diagnosis of MetS for predicting both diseases. Furthermore, Kahn and colleagues criticized the imprecise definition, underlining the lack of a definite pathogenesis of MetS, and expressed doubts about the clinical usefulness of MetS as a CVD risk marker (20,21). A joint report on MetS from the American Diabetes Association and the European Association for the Study of Diabetes identified 8 major concerns about MetS and made recommendations concerning the identification and treatment of CVD risk factors rather than simple labeling of patients with the term "metabolic syndrome" (21).
In the general population, MetS has been used in many clinical studies as a cluster of risk factors for diabetes and CVD, but no standardized definition of MetS nor any concrete evidence of a single underlying pathologic process have been developed, and a scientific debate still persists regarding the clinical usefulness of MetS.
|
DEFINITION AND CLINICAL IMPACT OF MetS IN CKD |
|---|
|
TOPABSTRACTDEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...CONCLUSIONSREFERENCES |
|---|
In earlier stages of CKD, several of the components of MetS are already common because they are risk factors for developing CKD or because they are consequence of CKD. During later stages of CKD, uremia is tightly linked to further metabolic and hemodynamic derangements such as glucose intolerance or insulin resistance (or both), high blood pressure, and dyslipidemia. In other words, advanced CKD and MetS have multiple similarities and multiple interactions, including glucose intolerance or insulin resistance, hypertension, atherogenic dyslipidemia, proinflammatory or prothrombotic state, and (in some individuals) obesity. In addition to these components of MetS, uremic toxicity in CKD may further increase the metabolic susceptibility of obesity-induced MetS by aggravating inflammatory and oxidative stress.
As previously described, insulin resistance and abdominal obesity are central components in MetS. Uremic toxins per se are thought to cause an acquired defect in the insulin-receptor signaling pathway (28,29), and increased inflammation in uremia further aggravates insulin resistance (30,31). Furthermore, insulin resistance, followed by glucose intolerance and hyperglycemia, also contributes to inflammation by increasing oxidative stress (32). Moreover, insulin resistance has been regarded as a contributor to enhanced protein catabolism (33). In a cross-sectional study with nondiabetic end-stage renal disease (ESRD) patients, higher insulin resistance assessed by the homeostasis model (HOMA-IR) was associated with muscle wasting (34). In nondiabetic hemodialysis (HD) patients, insulin resistance assessed by HOMA-IR was an independent predictor of cardiovascular mortality (35).
Obesity assessed by body mass index (BMI) is a risk factor for mortality in the general population, but as is the case with several of the other components of MetS, it is a poor predictor of mortality in ESRD patients. Obesity is not a typical finding in CKD patients, although the prevalence of obesity is increased in CKD patients. Chronic kidney disease has been proposed to be a state of MetS without obesity, but with malnutrition or wasting (or both) as a more typical component (36). In a study on ESRD patients starting dialysis, malnutrition was common even in those who were overweight (BMI > 25 kg2/m2), and it was associated with high body fat mass, low lean body mass, and inflammation (37). Overweight patients with protein–energy wasting (representing a state of obese sarcopenia) had a higher degree of inflammation, a greater body fat mass, a lower lean body mass, and a worse survival rate than did patients without wasting. The effect of obesity should therefore be considered together with other risk factors such as inflammation and malnutrition or wasting in ESRD patients.
It should also be noted that truncal fat mass rather than total fat mass
contributes to increased inflammation, as reported in a cross-sectional study
with ESRD patients (38). In
that study, truncal fat mass was positively correlated with plasma interleukin
6 (IL-6) and high-sensitivity C-reactive protein, and plasma IL-6 was also
negatively correlated with HDL cholesterol and apolipoprotein(a), suggesting
that, as a part of MetS in ESRD patients, truncal fat mass increases
inflammation and subsequently is associated with atherogenic lipoprotein
abnormalities. Truncal fat mass is associated with an adipokine imbalance; an
increase of leptin, resistin, tumor necrosis factor 
, and IL-6; and a
decrease of adiponectin, and thereby, it may contribute to endothelial
dysfunction, inflammation, oxidative stress, vascular calcification, increased
resting energy expenditure, insulin resistance, and muscle breakdown
(39).
On the other hand, fat mass can also be beneficial for CKD patients in its capacity as energy store and in its associations with genetic traits that have beneficial effects [such as genetic variation linked to increased circulating fetuin-A levels (40), higher bone mineral density (41), more efficient disposal of lipophilic toxins, and better stem cell mobilization (42)]. The effect of obesity should therefore be considered in the context of other metabolic abnormalities in ESRD patients.
Patients with ESRD often have hypertriglyceridemia and low HDL cholesterol, similar to the components of MetS, because of impaired enzymatic activity of lipoprotein lipase, hepatic triglyceride lipase, and lecithin–cholesterol acyltransferase. Several studies suggest an inverse correlation between total cholesterol and mortality risk in ESRD patients (43–45). However, in a study with ESRD patients starting dialysis, this association remained significant in the subgroup with inflammation or malnutrition (or both); in the subgroup without inflammation or malnutrition, a positive association between total cholesterol and mortality was observed (46).
In a randomized controlled trial with 200 advanced (stages 4 and 5) CKD patients, the overall prevalence of MetS by the WHO definition was 30.5%, and in the included PD patients, it was as high as 50% (47). In that study, MetS was associated with older age, the use of PD, ethnicity, increased oxidative stress, lower serum adiponectin, and increased risk of future cardiovascular events. Analyzing the individual components of MetS, the high prevalence of MetS in PD was associated mainly with a higher prevalence of insulin resistance (fasting glucose level). Intensive risk-factor modifications targeting high low-density lipoprotein cholesterol, hyperhomocysteinemia, anemia, and hyperphosphatemia in patients with MetS could not improve cardiovascular survival during a median follow-up period of 22 months (47).
In another retrospective, cross-sectional study of incident dialysis patients in the United States, the prevalence of MetS was 69.3% by the NECP-ATP III definition (48). In a study in Greek patients (49), the prevalence of MetS in HD patients using the NCEP-ATP III definition was 40.2%. The prevalence declined over time on HD, suggesting that HD might improve glucose tolerance, hypertension, and lipid disorders, and result in weight reduction. Also, the patients with MetS had better nutrition status as assessed by a full-scale nutrition assessment according to the guidelines from the Kidney Disease Outcomes Quality Initiative. However, the prevalence of CVD was not different between patients with and without MetS, suggesting that patients with MetS might not be protected from CVD, although they were well-nourished and had been on dialysis for a shorter time.
It should be noted that there is still no proven evidence that MetS, as a cluster of risk factors, would better predict cardiovascular mortality in ESRD patients than do the individual components of MetS in these patients. On the contrary, individual MetS risk factors such as blood pressure, serum cholesterol, and BMI are in fact paradoxically associated with improved survival in ESRD patients. It is not yet clear whether a diagnosis of MetS, using the same criteria as in the general population, is also associated with a paradoxical survival benefit or whether MetS is clinically more useful than an assessment of individual risk factors in CKD patients. Moreover, considering the paradoxical association of some of the risk factors in MetS with mortality, the additional risk factors specific to CKD patients (such as inflammation and malnutrition or wasting) should be taken account in all risk assessments in this group of patients. Also, the appropriate cut-off levels in the individual components of MetS and the benefit of interventions targeting these risk factors should be further evaluated in clinical studies.
|
DEFINITION AND CLINICAL IMPACT OF MetS IN PD |
|---|
|
TOPABSTRACTDEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...CONCLUSIONSREFERENCES |
|---|
Metabolic complications are a major issue in conventional PD using fluid with glucose as the osmotic agent. According to an earlier study by Delarue et al. (52), 60%–80% of the glucose delivered into the peritoneal cavity is absorbed through the peritoneum. Increased glucose absorption may lead to obesity and hyperglycemia as well as hyperlipidemia, which are components of MetS. Although glucose loading may worsen insulin sensitivity, insulin resistance after the start of PD was improved to a point similar to that seen in HD patients (53). In addition, the use of icodextrin could increase insulin sensitivity and plasma adiponectin levels in continuous ambulatory PD patients (54,55).
Although glucose-based dialysis fluids may contribute to obesity in PD patients, the manner in which this contribution affects clinical outcomes is not clear. In a study of prevalent PD patients followed for 1 year, 53% of PD patients gained body fat as assessed by dual-energy X-ray absorptiometry (56). Neither glucose absorption nor C-reactive protein was associated with the change of body fat, but baseline BMI was independently associated with a gain of body fat in this study. High BMI has been considered to be associated with reduced mortality in HD patients, but the association between BMI and clinical outcome in PD patients is not well established (57–59). Obese PD patients have a higher rate of peritonitis (60), and patients with low muscle mass and obesity defined as high BMI had higher cardiovascular mortality (61). Few data show an association between body fat or truncal fat change and mortality in PD patients, but loss of body fat was found to be a risk factor for all-cause mortality (62), and reduced limb-to-trunk lean mass ratio in men and low fat percentage in trunk in women were significant predictors of all-cause mortality in HD patients (63).
Another risk factor, dyslipidemia, may also worsen after PD because of intraperitoneal glucose load and protein loss, but the association between dyslipidemia and outcome must be considered in the context of malnutrition or inflammation in PD patients (45,46).
Apart from these individual risk factors of MetS, few studies have evaluated the predictive power of MetS as a cluster of risk factor for new-onset diabetes or cardiovascular or all-cause mortality in PD patients. There may be reasons for this lack of evidence in PD. First, the definition of MetS in PD patients has certain limitations: for example, measurement of waist circumference can vay according to the intraperitoneal dialysate volume or residual volume after dialysate drainage. Also, determination of plasma glucose or insulin status is difficult to standardize because of the inherent continuous absorption of glucose from dialysate in PD patients, resulting in hyperglycemia and hyperinsulinemia. Whether it is better or neutral to exclude certain criteria such as blood glucose or waist circumference is uncertain, as is whether a choice should be made to use other parameters such as Hb1Ac and BMI as replacements for the foregoing criteria in PD patients even if increased waist circumference and higher blood glucose levels represent the natural situation in PD. Second, some risk factors in MetS, such as cholesterol or BMI, are associated with a better nutrition status, and therefore are most likely paradoxically associated with better clinical outcomes in PD patients. Thus, no evidence exists that the use of the whole cluster of risk factors in MetS is better than assessing each risk factor separately in PD patients.
If the current definition of MetS in the general population were to be applied, more than 50% of PD patients would be defined as having MetS. However, no standardized definition of MetS exists, and few valid outcome data are available showing the consequences of a cluster of risk factors in PD patients. Patients on PD are indeed highly susceptible to the metabolic complications included in MetS, but no evidence exists to support a conclusion that these complications separately predict increased risk in the PD patients, and no data support a conclusion that the use of a diagnosis of MetS would be better than using each risk factor separately in PD patients. Therefore, rather than use clustered risk factors with unproven cut-off values, specific risk factors should instead receive careful consideration for potential paradoxical associations with mortality. Targeted treatment based on clinical evidence may be more valuable in PD patients. Also, metabolic risk assessment, combined with parameters of inflammation and nutrition, would be better for predicting CVD or mortality in PD patients.
|
CONCLUSIONS |
|---|
|
TOPABSTRACTDEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...CONCLUSIONSREFERENCES |
|---|
|
ACKNOWLEDGMENTS |
|---|
|
REFERENCES |
|---|
|
TOPABSTRACTDEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...DEFINITION AND CLINICAL IMPACT...CONCLUSIONSREFERENCES |
|---|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
