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Dr Arun Chawla discusses Obe
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Reverse Epidemiology Reverse Epidemiology of Obesity in Kidney of Obesity in Kidney
DiseaseDisease
Arun Chawla, MDArun Chawla, MDHofstra NSLIJ School of MedicineHofstra NSLIJ School of Medicine
OverviewOverview Conventional epidemiology Conventional epidemiology
ParadoxParadox
Studies…Studies…
Hypothesis to explain what we seeHypothesis to explain what we see
Paradox within paradoxParadox within paradox
What we all know… for What we all know… for Now!Now! In US, obesity is the second leading
cause of preventable disease and death.
Associated with ESRD - type 2 DM and hypertension.
Epidemic growing and life expectancy being shortened.
Prevalence of overweight, obesity and extreme obesity among adults: United States, trends
1976-80 through 2005-2006
NHANES December 2008
Associated co Associated co morbiditiesmorbidities Type 2 DMType 2 DM HypertensionHypertension CADCAD GDMGDM OSAOSA HyperlipidemiaHyperlipidemia OAOA GERDGERD GoutGout
ESRDESRD NASHNASH VTEVTE CholelithiasisCholelithiasis DepressionDepression Pulmonary Pulmonary
HypertensionHypertension CTSCTS InfertilityInfertility Breast/Colon cancerBreast/Colon cancer
Multivariate Relative Risk of Death from CVD, Cancer, and All Other Causes among Men and Women Who Had Never
Smoked and Who Had No History of Disease at Enrollment, According to BMI
Calle E et al. Nejm 1999
Multivariate Relative Risk of Death from All Causes among Men and Women According to BMI, Smoking, and
Disease Status
Calle E et al. N Engl J Med 1999
So…So…
Given the magnitude of the risks of obesity in the general population, it is important to clarify whether these risks apply to patients on dialysis, who have an overall cardiovascular risk at least 10 times greater than the general population.
First thoughts….First thoughts….
In 1982, Degoulet et al looked into In 1982, Degoulet et al looked into 1453 subjects treated in 33 French 1453 subjects treated in 33 French dialysis centers over 5 years and dialysis centers over 5 years and noticed no increase in mortality with noticed no increase in mortality with higher BMI. higher BMI.
After 17 years came the first trial …After 17 years came the first trial …
Influence of Excess Weight Influence of Excess Weight on Mortality and Hospital on Mortality and Hospital
Stay in 1346 Dialysis Stay in 1346 Dialysis PatientsPatients
Fleishmann and SalahudeenFleishmann and Salahudeen
Kidney International 1999Kidney International 1999
MethodsMethods
Cohort of 1346 HD patients in Cohort of 1346 HD patients in MississippiMississippi
Followed prospectively for 1 year for Followed prospectively for 1 year for hospitalization and mortality.hospitalization and mortality.
On dialysis for more than 90 days On dialysis for more than 90 days (avg. 4.3 yrs) (avg. 4.3 yrs)
38% had BMI >27.538% had BMI >27.5 13% had BMI <2013% had BMI <20 Normal considered BMI of 20-27.5Normal considered BMI of 20-27.5
De
ath
Ha
zard
Kaplan-Meier Death Kaplan-Meier Death HazardHazard
Fleishmann et al. Kidney Fleishmann et al. Kidney International 1999International 1999
Survival in days
400
ResultsResults Causes of death similar amongst three Causes of death similar amongst three
groups.groups.
For a unit increase in BMI>27.5 risk of For a unit increase in BMI>27.5 risk of dying showed RR reduction of 6% in the dying showed RR reduction of 6% in the univariate model and 4% in the univariate model and 4% in the multivariate model. multivariate model.
With 1 unit decrease of BMI below 20 the With 1 unit decrease of BMI below 20 the risk of death increased by 1.6 fold.risk of death increased by 1.6 fold.
Hospital admission rate per Hospital admission rate per year and Length of stay (LOS)year and Length of stay (LOS)
……
0
2
4
6
8
10
12
14
16
underweight Normalweight
Overweight
LOS
00.20.40.60.8
11.21.41.61.8
Underweight OverweightAd Rate
ConclusionConclusion
Special attention to nutrition to Special attention to nutrition to achieve high end of normal BMI achieve high end of normal BMI maymay help to reduce morbidity and help to reduce morbidity and mortality in hemodialysis patients. mortality in hemodialysis patients.
Results were significant even after adjustment for markers of nutrition like albumin, transferrin and creatinine.
LimitationsLimitations
Smaller sample sizeSmaller sample size Mainly AA (88%) so no diversityMainly AA (88%) so no diversity Survival advantage not shown in Survival advantage not shown in
CaucasiansCaucasians Short follow upShort follow up BMI not the most accurate tool to BMI not the most accurate tool to
comment on body composition.comment on body composition.
Questions Fat mass or muscle mass?
Mild versus severe obesity?
Is high BMI only a short term advantage?
Interactions of race or ethnicity?
Association of Body Size with Outcomes Among Patients
Beginning Dialysis
Johansen Et alAm J Clinical Nutrition 2004
HypothesisHypothesis
Extremely high BMI would not be associated with increased survival time.
If there were a survival advantage at
higher BMI, it would be explained in part by the increased lean body mass (LBM) that usually accompanies high BMI.
MethodsMethods
Data obtained from the USRDS and CMS.
Mortality, hospitalisation and dialysis modality (i.e. HD or PD), for adult patients beginning dialysis between April 1, 1995, and November 30, 2000.
Follow-up extended through November 30, 2001 with median follow up of 2 years.
Johansen, K. L et al. Am J Clin Nutr 2004
FIGURE 1. Hazard ratios for death among men ({blacksquare}) and women ({square}) by category of BMI (A), Benn index (B), and estimated fat mass (C)
Copyright ©2004 The American Society for Nutrition BMI
De
ath
Ha
zard
Effects of adjustment for serum creatinine and creatinine index on the relation
between BMI and survival
Curve more steep!!!
Copyright ©2004 The American Society for Nutrition Johansen, K. L et al. Am J Clin Nutr 2004
BMI
De
ath
Ha
zard
ConclusionsConclusions BOTH THEIR HYPOTHESIS PROVED
WRONG!!!
Higher adiposity was associated with increased survival, even after adjustment for demographics, laboratory values, comorbidities, dialysis modality and even when adiposity was assessed by different methods.
Pattern observed even for cardiovascular death
Less evident with PD and Asians
StrengthsStrengths
Large sample sizeLarge sample size Complete dataComplete data All racial and ethnic groupsAll racial and ethnic groups Longer follow upLonger follow up
LimitationsLimitations Observational designObservational design
Does weight gain Does weight gain help????help????
Association of Morbid Obesity and Weight Change Over Time with Cardiovascular Survival in
Hemodialysis PopulationK.Kalantar-Zadeh et al
Am J Kidney Diseases 2005
MethodsMethods
Patients enrolled with DA Vita IncPatients enrolled with DA Vita Inc Cohort on July 1, 2001 and subsequently Cohort on July 1, 2001 and subsequently
patients were enrolled through June 30, patients were enrolled through June 30, 2003- Non-concurrent cohort.2003- Non-concurrent cohort.
Data organized to form “8 quarterly mean Data organized to form “8 quarterly mean values” to include 2 year observation values” to include 2 year observation period.period.
11 categories <18, >45 and then 9 in 11 categories <18, >45 and then 9 in between 18 and 44.9between 18 and 44.9
Data collectedData collected CBC, BUN, Hemoglobin, albumin,
creatinine (muscle mass), dialysis dose and ferritin and TIBC (marker of nutrition).
For each analysis, 3 models were examined based on the level of multivariate adjustment:
(1) the unadjusted model (2) case-mix– adjusted models (3) case-mix and laboratory
K.Kalantar-Zadeh et al Am J Kidney Dis 2005
39.3
15.9
Fig 1. Time-dependent association between BMI and 2-year all-cause mortality in 54,535
MHD patients
K.Kalantar-Zadeh et al Am J Kidney Dis 2005
Fig 2. Time-dependent association between BMI and 2-year cardiovascular mortality in
54,535 MHD patients
K.Kalantar-Zadeh et al Am J Kidney Dis 2005
Fig 4. Association between the rate of weight change over time and subsequent all-cause
mortality in 46,629 HD patients
K.Kalantar-Zadeh et al Am J Kidney Dis 2005
P<.0001
p.068
Fig 5. Association between the rate of weight change over time and cardiovascular mortality
in 46,629 HD patients
K.Kalantar-Zadeh et al Am J Kidney Dis 2005
P .101
P<.001
ConclusionsConclusions Even after exhaustive adjustment for time
varying laboratory markers, both all-cause and cardiovascular mortality showed decreasing rates across increasing BMI categories, even morbid obesity.
Lower BMI at baseline consistently is found to be a strong predictor of elevated mortality.
Weight loss was associated with increased CV and all-cause death, whereas weight gain showed a trend toward improved survival and reduced cardiovascular mortality.
Association Of Low Body Mass Index Association Of Low Body Mass Index And Weight Loss With Increased And Weight Loss With Increased
Mortality In 14,065 Transplant-wait-Mortality In 14,065 Transplant-wait-listed Hemodialysis Patients listed Hemodialysis Patients
K.Kalantar-Zadeh et al Circulation 2008
What we Now know…!What we Now know…!
Leavey SF, et al. Body mass index and mortality in ‘healthier’ as compared with' sicker’ hemodialysis patients: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrology Dial Transplant 2001
??????????
Hypothesis Hypothesis
TNF- alpha receptors in obesity
TNF-alpha is elevated in CHF and in dialysis patients and may contribute to cardiac injury through its pro-apoptotic and negative inotropic effects (2).
Adipose tissue produces soluble TNF-alpha receptors, which may play a cardio-protective role.
2) Feldman et al. The role of tumor necrosis factor in the pathophysiology of heart failure. J Am Coll Cardiol 2000; 35:537– 44.
Neurohormonal Neurohormonal alterationsalterations
The lean subjects had significantly higher increases in plasma adrenaline and renin concentrations during treadmill testing, despite similar baseline values and a history of hypertension (3).
Heightened sympathetic and renin-angiotensin activities are associated with a poor prognosis in heart failure and fluid overload states (such as those seen in dialysis patients).
3) Weber MA et al. Contrasting clinical properties and exercise responses in obese and lean hypertensive patients. J Am Coll Cardiol 2001;37:169 –74
Selection BiasSelection Bias
More stable More stable hemodynamic status hemodynamic status
Despite having similar PCWP and cardiac indexes, overweight and obese patients with fluid overload tend to have higher systemic blood pressure values (1).
Due to better tolerance larger proportion of obese and overweight patients take ACE inhibitors.
1) Horwich et al . The relationship between obesity and mortality in patientswith heartfailure. J Am Coll Cardiol 2001;38:789 –9
Endotoxin-lipoprotein hypothesis
Lower serum total cholesterol and lipoprotein concentrations are strongly and independently associated with impaired survival in dialysis (4).
It reflects a richer pool of internal lipoproteins that can actively bind to and remove circulating endotoxins, which effectively retards inflammation and subsequent atherosclerosis (5).4) Nishizawa et al. Paradox of risk factors for cardiovascular mortality in uremia: is a higher cholesterol level better for atherosclerosis in uremia? Am J Kidney Dis 2001;38 S4–7.5) Niebauer J, et al. Endotoxin and immune activation in chronic heart failure: a prospective cohort study. Lancet 1999;353: 1838–42.
Malnutrition-inflammation complex syndrome – “Cachexia in slow motion” Undernourished people more likely to develop PEM Undernourished people more likely to develop PEM
and slow to recover form illnesses and its and slow to recover form illnesses and its complications.complications.
Increased release of IL-6 and TNF may suppress appetite (6), may cause muscle proteolysis and hypoalbuminemia, and may be involved in the processes that lead to atherosclerosis.
Patients with lower albumin, low cholesterol, creatinine and homocysteine concentration might represent MICS making them prone to infection and inflammation and slower recovery.
Nutritional Inflammatory hypothesis (6) Kalantar-Zadeh K. Appetite and inflammation, nutrition, anemia, and clinical outcome in hemodialysis patients. Am J Clin Nutr 2004.
Time discrepancies among competitive risk factors
US population US population versus developing versus developing countriescountries
Survival advantages that exist in obese dialysis patients may, in the short term, outweigh the harmful effects of these risk factors on CVD in the long term.
Dialysis patients, ironically, do not live long enough to die of the consequences of overnutrition!
Dialysis modality????Dialysis modality????
Body mass index, Dialysis Modality, and Survival: Analysis of the United States Renal Data System Dialysis Morbidity and
Mortality Wave II Study
ABBOTT et alKIDNEY INT. 2004
Methods
Retrospective cohort study of the USRDS DMMS Wave II database.
Patients who started dialysis in 1996 and were followed until October 2001 (5yrs)
Outcome: Mortality Divided into 4 groups 1 (<21.9), 2
(21.9-24.9), 3 (25-29.9) and 4(>29.9)
PD patients-PD patients-Less AA, younger, more renal transplant.Less AA, younger, more renal transplant.
Decreased prevalence of CAD, CHF, CVA, Decreased prevalence of CAD, CHF, CVA, LVH on EKG, PVD, and cancer.LVH on EKG, PVD, and cancer.
Decreased Erythropoietin useDecreased Erythropoietin use
Higher ACE, statins and beta blockers.Higher ACE, statins and beta blockers.
No significant difference of BMINo significant difference of BMI
Demographic and clinical Demographic and clinical variablesvariables
PD patientsPD patients
In the lowest group were at an increased risk of mortality
Kaplan-Meier plot of patient survival by BMI
HD survival 39.8% vs. 32.3% PD survival 38.7% vs. 40.4%
ABBOTT et al KIDNEY INT. 65
ConclusionsConclusions
Low BMI was independently associated with higher mortality regardless of dialysis modality.
HR for death in patients with BMI >30 was 0.89 for HD and significant while no such correlation in PD patients.
““Paradox within Paradox within paradox”paradox” Adequacy of dialysis not known.Adequacy of dialysis not known.
Whether “uremic” and “inflammatory” Whether “uremic” and “inflammatory” malnutrition differ by dialysis modality has malnutrition differ by dialysis modality has not been established.not been established.
Changes in B.P, med use, lab values etc. Changes in B.P, med use, lab values etc. wasn’t followed.wasn’t followed.
1.5–4.25% of dextrose in their peritoneal dialysate (often around the clock), which is estimated to be absorbed at 45%.
CKD patients??CKD patients??
(1) Evans et al; Natural history of chronic reanl failure; Results from an unselected population cohort in Sweden. AJKD 2005
(2) BMI and mortality in CKD. Madero et al Abstract JASN 2006
(3) Reverse epidemiology in patients with CKD. Kovesdy etal. Seminars in Dialysis 2007