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Pediatric Nephrology

, Volume 34, Issue 12, pp 2457–2477 | Cite as

Can childhood obesity influence later chronic kidney disease?

  • Lyda Jadresic
  • Richard J. Silverwood
  • Sanjay Kinra
  • Dorothea NitschEmail author
Review
Part of the following topical collections:
  1. What’s New in Chronic Kidney Disease

Abstract

Childhood overweight and obesity affects more and more children. Whilst associations of childhood overweight with later outcomes such as hypertension, diabetes and cardiovascular disease have been well documented, less is known about the association of childhood overweight and obesity with kidney disease. We review the existing evidence for the association of childhood obesity with markers of childhood and adult kidney disease. Whilst there is some evidence for an association, studies have not been able to distinguish between childhood being a sensitive time to develop later kidney problems, or whether observed associations of childhood obesity with poor outcomes are driven by greater lifelong exposure to obesity.

Keywords

Body mass index Childhood Obesity Overweight Kidney disease Review 

Introduction

Over the last two to three decades, the prevalence of childhood overweight and obesity has increased in many parts of the world [1, 2]. In developed countries, the prevalence of overweight or obesity has increased from 6.9% in boys and 16.2% in girls in 1980 to 23.8% in boys and 22.6% in girls in 2013. Over a similar time span, in developing countries, the prevalence of overweight or obesity increased from 8.1% in boys and 8.4% in girls in 1980, to 12.9% in boys and 13.4% in girls [3]. The majority of overweight or obese children live in developing countries, where the rate of increase has been more than 30% higher than that in developed countries [4].

Adulthood obesity has been identified as a risk factor for a variety of chronic conditions including hypertension, diabetes, heart disease, high cholesterol, stroke, and certain cancers [5, 6]. Tracking of obesity from childhood into adulthood is acknowledged [7]. Correspondingly, research has also focused on the impact of childhood obesity on morbidity and premature mortality in adulthood [8, 9].

A growing number of studies have concluded that adulthood obesity increases the risk for kidney disease [10]. This may be a secondary effect after the development of diabetes and/or hypertension. A recent meta-analysis of 39 cohorts involving 630,677 participants who at baseline had no evidence of chronic kidney disease (CKD) and who had had a mean follow-up of 6.8 years [11], found that obesity increased the relative risk (RR, 95% Confidence Interval) of developing low estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2): 1.28, 1.07–1.54 and albuminuria (1+ at dipstick or an albumin creatinine ratio of ≥ 3.4 mg/mmol): 1.51, 1.36–1.67. There is weak evidence in adults that bariatric surgery may improve renal function [12], but there have been no randomised controlled trials and there is a lack of long-term follow-up studies [13].

These findings may lead us to believe that childhood overweight or obesity will similarly increase the risk of adulthood kidney disease. However, few studies have directly examined this, and consequently, our understanding is limited.

The aim of this review is to see whether there is any evidence for an association between obesity during childhood and adult CKD. This included examining the evidence for an association of obesity with renal function markers in childhood (including a discussion of the study designs involved and measurements used), renal function post paediatric renal transplantation that compare obese to non-obese recipients, the evidence for the effect of bariatric surgery on renal function in the paediatric age, before moving on to studies that examined the association of childhood obesity with adult CKD. Relevant literature was identified in PubMed, Medline, Embase and Scopus using the following terms, as well as MESH terms for: Obesity OR overweight OR weight gain OR body mass index OR metabolic syndrome AND Child OR children OR paediatric OR paediatrics OR adolescence OR teenage OR teenagers OR young AND microalbuminuria OR albuminuria Or proteinuria OR hyperfiltration OR glomerular filtration rate OR kidney OR renal, and included only studies that had a full text available in English, leaving out numerous conference abstracts.

Studies in the paediatric age on the association of obesity with early markers of kidney disease

Discussion of study designs used in the paediatric age group

Studies carried out in paediatric populations on the possible association between childhood obesity and early changes in renal filtration and microalbuminuria are listed in Tables 1 and 2 respectively, and consisted of cross-sectional studies, “case-control” studies and case series reports. The majority of studies have a cross-sectional design where obesity and renal function are measured at the same point in time. A key step in establishing a causal role for obesity is that it should precede the measured effect on renal function. Therefore, cross-sectional studies make it hard to attribute a causal effect of obesity on kidney function. Other studies are described as “case-control studies” and typically compared renal function in children who are obese (labelled by authors as “cases”) to renal function in children who are not obese (labelled by authors as “controls”). We have used inverted commas, as this design is not a standard case-control design as defined by epidemiologists who would recruit cases (those with renal disease) and controls (those without renal disease) and investigate prevalence of recorded pre-existing obesity before the onset of renal disease. In all “case-control” studies on this topic however, the same problem arises as for the cross-sectional studies—renal function is measured at the same time as the obesity status and therefore there cannot be any inference made on the direction of causality. In addition, there are many reasons to doubt whether participants recruited from obesity clinics and their comparator groups are truly representative (i.e. findings generalizable to the general population), and whether the comparisons made are valid, i.e. adjusted for confounding factors related to attending obesity clinics and factors related to recruiting controls. They are joined by a number of case series reports, which provide no comparison group. Well-powered prospective population-based cohort studies would provide an opportunity to measure obesity prior to the onset of renal function abnormalities. They are likely to be less biased than case-control studies, provided that losses to follow up are kept to a minimum. Unfortunately, to our knowledge, no such cohort studies have been carried out to date.
Table 1

Summary of studies on childhood obesity and early changes to GFR detected in childhood

Author, year of publication

n

Age in years

Sampling

Exposure

Outcome*

Findings

Direction of association between obesity and eGFR

Cross-sectional studies

Lee, 2017 [14]

Population based

2584

12–17

National Health and Nutrition Examination

Survey (NHANES)

population survey, USA

Metabolic risk factors including BMI z score, derived from own data

Hyperfiltration defined as eGFR ≥ 120 mL/min/1.73 m2.

Creatinine-based eGFR (ml/min/1.73 m2), Schwartz equation:

0.413 × height (cm)/serum creatinine (mg/dL)

Outcome measure:

Adjusted Odds Ratio for hyperfiltration

BMI z score OR for hyperfiltration adjusted for sex, age, race and income:

0.96 (0.82–1.12), p = 0.578

=

Tajbakhsh, 2017 [15]

367

10–18

Random sampling from national survey of 5625 rural and urban school based children:

BMI

Creatinine-based e GFR (ml/min/1.73 m2), Schwartz equation:

BMI:

 

Population based

Childhood and Adolescence Surveillance and Prevention of Adult Noncommunicable Disease III (CASPIAN III), Iran

Waist circumference

0.413 × height (cm)/ serum creatinine (mg/dL)

r = 0.121, p = 0.02

+

Waist-height ratio

Outcome measure:

Waist circumference:

 

Correlation coefficient

r = 0.190, p < .001

+

Waist-height ratio:

 

r = 0.070, p = 0.18

=

Marmarinos, 2016 [16]

Population based

536

4–17

Teaching hospital paediatric clinic, excluding children with chronic conditions or on anti-inflammatory agents, diuretics, anticonvulsants and antibiotics, Greece.

BMI

Cystatin-C (mg/L)

Outcome measure:

Multivariate regression coefficient

Regression coefficient for BMI (β) adjusted for sex, age (in a variable category that indicates the existence of puberty) and blood pressure:

β: + 0.03 (0.01, 0.04) p = 0.001

Correia-Costa, 2015 [17]

313

8–9

Birth cohort Generation XXI, Portugal

BMI z-score based on WHO growth data

Creatinine-based and cysteine-C based eGFR: using Schwartz, Filler, Zappitelli-Comb, Schwartz-Comb formulae (ml/min/1.73 m2) and 24 h creatinine clearance (Cr Cl).

Regression coefficient (β) for BMI z score, adjusted for age and sex:

 

Population based

Outcome measure:

eGFR Schwartz-R:

=

Multivariate linear regression coefficient

− 0.51 (− 2.17–1.16), p = 0.548

 

Filler eGFR:

− 4.26

 

(− 5.77 to − 2.74), p < 0.001

 

Zapitelli-Comb eGFR:

− 2.77

 

(− 4.16 to − 1.38), p < 0.001

 

Schwartz-Comb eGFR:

− 1.10

 

(− 2.01 to − 0.18), p = 0.014.

 

24 h Cr Cl:

−3.54

 

(−6.52 to −0.57), p = 0.020

 

Di Bonito, 2014 [18]

Population based

901

6–16

General paediatric clinic with diagnosis of allergy, gastro-oesophageal reflux, overweight, obesity, excluding chronic liver, renal cardiac, endocrine and infection conditions, Italy

BMI

Creatinine-based eGFR (ml/min/1.73 m2), Schwartz formula:

0.413 x height (cm)/serum creatinine (mg = dl).

eGFR: mild-low eGFR (< 20th percentile), high eGFR (> 80th percentile), and intermediate eGFR (20-80th percentile and considered as the reference category)

Outcome measure: Comparison of mean BMI

BMI in:

low-mid eGFR = 26 ± 6

Intermediate eGFR = 25 ± 6

high eGFR

26 ± 6, p = 0.10

=

Ma, 2014 [19]

5222

6–17

Stratified sampling by ethnic group, China

BMI

Creatinine (μM)

Regression coefficient for BMI (β) adjusted for uric acid, glucose, triglycerides, cholesterol, high and low-density cholesterol (but not height):

 

Population based

Outcome measure: Multivariate linear regression coefficient

β (6–7 years) = − 0.113 (SE 0.265), p = 0.479

=

β (8–9 years) = − 0.37 (SE 0.284), p = 0.313

=

β (10–11 years) = 0.041(SE 0.097), p = 0.042;

β (12–13 years) = 0.081 (SE 0.074), p < 0.001

β (14–15 years) = 0.18 (SE 0.093), p < 0.001,

β (16 – 17y) = 0.171(SE 0.22), p < 0.01.

Soylemezoglu, 2012 [20]

Population based

3079

5–18

National population survey, cluster sampling, Turkey

BMI categories based on CDC growth data [21].

Creatinine-based eGFR(ml/min/1.73 m2), Schwartz formula:

k × length (cm)/creatinine (mg/dL) where k = 0.70 in males ≥ 13 years and 0.55 for the rest

Outcome measure: Comparison of mean eGFR

eGFR in:

Underweight:

133.30 ± 24.45

Normal:

129.19 ± 22.95

Overweight:

128.18 ± 22.79

Obese:

122.71 ± 21.64

(p < 0.001).

Koulouridis, 2010 [22]

166

3–18

Healthy children recruited from sport academies and through local media, Corfu

BMI

Creatinine-based eGFR (ml/min/1.73 m2), Schwartz formula:

eGFR correlation coefficient:

 

Population based

Waist circumference

k x height(cm)/creatinine (mg/dl) × 88.4 where k = 61.9 in males ≥ 13 years and 48.6 for the rest

with BMI:

 

Outcome measure: Correlation coefficient

r = 0.28, p = 0.0004

+

with waist circumference:

 

r = 0.419, p < 0.00001

+

Cindik, 2005 [23]

85

7–16

Obesity screening survey in two primary schools in two cities in two regions Turkey

BMI

Creatinine-based eGFR (mls/min), Leger formula:

eGFR in:

 

Population based

BMI categories

56.7 x weight (kg) + 0.142 x height2 (cm2)]/serum creatinine (μmol/l)

Obese:

 

Obese ≥ 95th, controls < 85th, using data from First National Health and Nutrition Examination Study, 1971 to 1974 [24]

Outcome measure: Comparison of mean eGFR

141.8 ± 48.2

 

Controls:

 

118.6 ± 28.4, p = 0.092.

=

Correlation between BMI and eGFR:

 

r = 0.39 p = 0.001

+

“Case1” control

Pacifico 2016 [25]

924

3–17

Hospital outpatient Clinics and from randomly selected local schools, Italy

BMI categories based on WHO growth data

Creatinine-based eGFR (ml/min/1.73 m2), Schwartz equation:

eGFR in:

 

“Case” control study

0.413 × height (cm)/ serum creatinine (mg/dL)

Obese:

 

Outcome measure: Comparison of mean BMI

115 (104–134)

 

Normal:

 

108 (100–118), p < 0.0001.

+

Numbers with:

 

eGFR < 90: Obese:

 

22 (6.7%)

 

Normal:

 

1 (0.77%), p < 0.01

eGFR > 139: mL/min/1.73 m2,

 

Obese:

 

56 (17.0%)

 

Normal:

 

6 (4.6%), p < 0.0001

+

Bartz, 2015 [26]

58

14–16

Medical centre and through media advertising, USA

BMI category and response to oral glucose tolerance test (OGT)

Creatinine-based e GFR (ml/min/1.73 m2): Schwartz equation:

eGFR in:

 

“Case” control study2

0.413 × height (cm)/ serum creatinine (mg/dL)

Normal BMI:

 

Outcome measure: Comparison of mean eGFR

94.0 ± 4.6

 

Overweight and normal OGT:

 

106.0 ± 3.9

 

Overweight and pre-DM:

 

108.3 ± 4.0.

 

Normal vs overweight-PreD, p = 0.049

+

Linear regression model:

 

percentage body fat β = 0.40, p = 0.03

+

Franchini 2015 [27]

318

6–18

Obesity Clinic, Diabetic clinic and from admissions to paediatric ward for minor trauma, fractures, Italy

BMI z score (based on Italian reference values)

Creatinine and cysteine-C based eGFR:

eGFR adjusted for age, sex, and pubertal stage:

 

“Case” control study

Bouvet formula:

Obese =

 

63.2 (1.2/cysteine)0.56 (1.09/creatinine)0.35 (weight/45)0.3 (age/14)0.4

116.15 ± 1.67

 

Outcome measures: Comparison of adjusted mean eGFR

Controls:

 

Multivariate linear regression coefficient

105.06 ± 1.82

 

p < 0.001

+

Type 1 DM with normal weight and obese group had similar degree of hyperfiltration

 

Linear regression coefficient (β) between BMI z-score and eGFR adjusted for age, sex and pubertal stage:

 

r = 0.328, p < 0.001

+

Falakaflaki, 2012 [28]

“Case” control study

92

7–12

School children, matched on age and sex,

Iran

BMI categories based on 2000 CDC growth data [21]

Creatinine-based e GFR (ml/min/1.73 m2), Schwartz equation:

k × height (cm)/ serum creatinine (mg/dL)

Outcome measure: Comparison of mean eGFR

Overweight:

98.90 ± 4.59

Normal:

96.37 ± 3.73, p = 0.49

=

Savino, 2011 [29]

“Case” control study

157

5–17

Obesity clinic, and from admissions to paediatric ward with minor illnesses, once recovered, Italy.

BMI categories: Obese= > 97th percentile for age and sex.

Normal: within 2 SD of the 50th centile

Creatinine-based eGFR (ml/min/1.73 m2), Schwartz formula:

k × length (cm)/creatinine (mg/dL) where k = 0.70 in males ≥ 13 years and 0.55 for the rest

Outcome measure: Comparison of mean eGFR

Obese:

144.3 ± 26.7

Normal:

136.8 ± 12.7, p = > 0.05

=

“Case3” series:

Xiao, 2014 [30]

“Case” series

242

≤ 19

Consecutive cases pre- bariatric surgery at each of five Teen-LABS centres, USA

BMI

Cystatin C-based eGFR (ml/min/1.73 m2):

Larsson formula

eGFR = 77.24 × (Cystatin-C)−1.2623

Outcome measure: Multivariate linear regression coefficient

Regression coefficient (β) for BMI adjusted for sex, age, race, DM, hypertension, HOMA-IR, HbA1c, ferritin, serum albumin, transferrin, dyslipidaemia, kidney stones:

β (per 10-unit increase in BMI): − 7.90 (− 11.60,− 4.20), p = < 0.01

BMI body mass index, CDC Centers for Disease Control and Prevention, DM diabetes mellitus, eGFR estimated glomerular filtration rate, HOMA-IR homeostatic model assessment of insulin resistance, SD standard deviation

* = Some studies may have been designed to assess another primary outcome; however, in this table, we have listed the renal outcome of interest for this systematic review

1 = cases defined by exposure = obesity

2 = cases defined by exposure, group 1 = overweight and normal oral glucose test (OGT), group 2 = overweight and pre-diabetes (impaired fasting glucose or impaired glucose tolerance)

3 = cases defined by exposure = obesity

Table 2

Summary of studies on childhood obesity and proteinuria detected in childhood

Author, year of publication, type of study

n

Age in years

Sampling

Exposure

Outcome*

Findings

Direction of association between obesity and proteinuria

Cross sectional studies:

Larkins [31], 2017

population based

975

5–18

Australian Health Survey stratified, multistage area design population survey

Overweight or obesity, based on WHO growth data

Microalbuminuria defined as UACR ≥ 30 mg/g or ≥ 3.4 mg/mmol

Outcome measure:

Adjusted odds ratio for microalbuminuria

Overweight/Obese OR, adjusted for age and sex:

0.34 (0.15–0.75), p = 0.009

Cho [32], 2017

1976

10–19

Korean National Health and Nutrition Examination Survey (KNHANES)

Obesity, based on Korean National Growth Data Obesity = BMI ≥ 95th percentile or ≥ 25 kg/m2

Microalbuminuria defined as UACR ≥ 30 mg/g and < 300 mg/g

Prevalence in:

 

population based

Outcome measures: Comparison of prevalence

Obese: 1.7%

 

Comparison of geometric means

Normal: 3.2% p = 0.238

=

Correlation coefficient

UACR (geometric mean ± SE):

 

Obese: 2.1 ± 0.2

 

Non-obese: 3.2 ± 0.1

 

p = 0.001

-

Univariate coefficient (r) (UACR log transformed v BMI-SDS):

 

r = − 0.19, p < 0.001

-

Machluf [33], 2016

population based

113,694

17

Conscripts National Military Service, Israel

Obesity, based on national growth data on BMI: Obesity: > 95th percentile

Normal 5th to < 85th

Proteinuria:

> 200 mg/24 h

Outcome measure:

Adjusted odds ratio for proteinuria

Obesity OR, adjusted for country of birth, country of origin, year of birth, religion, education, intelligence score, residential environment, family size, and blood pressure:

1.07 (0.59–1.94), p > 0.05

=

Gracchi [34] 2016

population based

1288

2– < 3

Prospective birth cohort study, Netherlands

BMI

Log UACR

Outcome measure:

Univariate linear regression coefficient

β = 0.04 (− 0.00–0.01), p = 0.063

=

Gurecka 2016 [35],

2666

14–20

State schools, Slovakia

BMI

Microalbuminuria defined as UACR 2.5–25.0 mg/mmol in boys, 3.5–35.0 mg/mmol in girls

Boys:

 

population based

BMI

Waist/Height ratio

Outcome measures:

BMI in:

-

Comparison of mean BMI

Normo-albuminuria group: 23.2 ± 3.8

 

Comparison of mean waist/height ratio

Microalbuminuria group: 21.8 ± 3.0,

 

p < 0.05

 

Waist/height ratio in:

-

Normo-albuminuria group: 0.45 ± 0.05

 

Microalbuminuria group: 0.42 ± 0.04,

 

p < 0.01

 

Girls:

 

BMI in:

=

Normo-albuminuria group: 22.0 ± 3.4

 

Microalbuminuria group: 21.0 ± 3.5,

 

p > 0.05

 

Waist/height ratio in:

=

Normo-albuminuria group: 0.43 ± 0.05

 

Microalbuminuria group: 0.43 ± 0.05

 

p > 0.05

 

Multivariate regression

Neither BMI, nor weight/height ratio nor other cardiovascular factors found to be associated with microalbuminuria in multivariate model in either gender

=

Rutkowski, 2013 [36]

population based

889

14–15

Independent schools, Poland

BMI

Albuminuria defined as UACR ≥ 30 mg

Correlation coefficient r

 
    

Waist to height ratio (WHtR)

Outcome measure:

BMI = − 0.198, p > 0.05

=

     

Correlation coefficient

WHtR = −0.151, p < 0.01

-

Okpere, 2012 [37]

population based

864

10–19

Secondary schools selected by multistage sampling, Nigeria

BMI categories based on CDC growth data [21]

Microalbuminuria defined by urine dipstick strip reading of 20 mg/L or more

Outcome measure:

Adjusted odds ratio for microalbuminuria

Obesity OR adjusted for DM, hypertension, FH of DM, FH of hypertension:

0.859 (0.474–1.555)

p > 0.05

=

Hirschler, 2010 [38]

1564

5–14

Random selection of schools, Argentina

BMI categories based on CDC growth data [21]

UACR

Log n UACR model, BMI β = − 0.03; p < 0.001) adjusted by gender, waist circumference, age and systolic and diastolic blood pressure.

-

population based

BMI

Natural log UACR (Log n UACR)

Outcome measure: Multivariate linear regression coefficient

Adjusted odds ratio for microalbuminuria

Adjusted OR for UACR ≥ 3rd quartile by gender, waist circumference, age and systolic and diastolic blood pressure in Overweight/Obese: 0.77 (0.64–0.92)

-

Haysom 2009 [39]

population based

2266

4–14

Government-run primary schools with high Aboriginal population, Australia

BMI SD quartiles based on international normative data SD (z) score

Albuminuria defined as UACR (mg/mmol) ≥ 3.4 mg/mmol (on urine dipstick)

Outcome measure:

Adjusted Odds Ratio for albuminuria

OR for albuminuria by BMI quartile adjusted for ethnicity, age, gender, birthweight, blood pressure and categories of isolation, disadvantage and region.

BMI SD − 4.8 to − 0.8:

OR = 1

BMI SD − 0.7 to 0.1:

OR = 0.67 (0.44–1.04)

BMI SD 0.2–0.7:

OR = 0.65 (0.42–1.02)

BMI SD 0.8–6.9:

OR = 0.51 (0.32–0.82) (p < 0.001)),

p for trend < 0.05

Haysom1 2009 [40]

population based

1432

4–14

2-year follow-up

Government-run primary schools with high aboriginal population, Australia

BMI SD (z score)

Albuminuria defined as albumin-creatinine ratio of 3.4 mg/mmol or greater.

Outcome measure:

Adjusted odds ratio for microalbuminuria

Prevalence of albuminuria:

Baseline: 7.3%

(Obesity baseline: 7.1%)

At 2 years: 1.5%

(Obesity at 2 years 5.3%)

OR for microalbuminuria adjusted for ethnicity, age, sex, birth weight, systolic blood pressure, diastolic blood pressure, isolation category, disadvantage category and school, at 2-year follow up:

BMI SD − 4.8 to − 0.8:

OR 1

BMI SD − 0.7 to 0.1:

OR 0.83 (0.24–2.96)

BMI SD 0.2–0.7:

OR 1.31 (0.31–5.57)

BMI SD 0.8–6.9:

0.15 (0.01–2.05,

p > 0.05

=

Haysom1 2009 [41]

population based

1506

4–14

4-year follow-up

Government-run primary schools with high Aboriginal population, Australia

Body mass index (BMI) SD (z Score)

Albuminuria defined as albumin-creatinine ratio of 3.4 mg/mmol or greater.

Outcome measure:

Adjusted odds ratio for microalbuminuria

Prevalence of albuminuria:

Baseline: 7.3%

(Obesity baseline: 7.1%)

At 4 years: 2.4%

(Obesity at 4 years 5.0%)

BMI z score OR for microalbuminuria adjusted for ethnicity, age, sex, birth weight, systolic blood pressure, diastolic blood pressure, isolation category, disadvantage category and school, at 4-year follow up:

BMI SD − 4.8 to − 0.8:

OR 1

BMI SD − 0.7 to 0.1:

OR 1.07 (0.45–2.54)

BMI SD 0.2–0.7:

OR 0.73 (0.28–1.87)

BMI SD 0.8–6.9:

OR 0.66 (0.25–1.75),

p > 0.05

=

Nguyen 2008 [42]

2515

12–19

National Health and Nutrition Examination Survey

Overweight = (BMI of = 95th percentile)

Microalbuminuria defined as UACR = 30 mg/g and < 300 mg/g

Prevalence of microalbuminuria:

non-Overweight = 8.7%

 

population based

Waist circumference

Outcome measure:

non-Overweight = 8.7%

 

Comparison of prevalence of microalbuminuria

Overweight = 0.3%,

 

p = 0 .005

-

Prevalence of microalbuminuria:

 

No abdominal obesity: 7.9%

 

Abdominal obesity: 1.0%,

 

p - 0.03

-

Jafar 2005 [43]

population based

3621

5–14

National Health Survey of Pakistan (NHSP) over 4 years (1990–1994) by the Pakistan Medical Research Council,

BMI

Proteinuria defined as ≥30 mg/dl (on urine dipstick)

Outcome measure:

Adjusted odds ratio for proteinuria

BMI OR for proteinuria adjusted by height, socio-economic status, ethnicity, urban dwelling, systolic blood pressure:

0.96 (0.90–1.02), p > 0.05

=

Cindik, 2005 [23]

population based

85

7–16

Obesity screening survey in two primary schools in two cities in two regions Turkey

BMI categories

Obese ≥ 95th, controls < 85th, using data from

First National Health and Nutrition Examination Study, 1971 to 1974 [24]

Urine microalbumin excretion (mg/24 h)

Outcome measure: Comparison of means of microalbuminuria

Urine microalbumin excretion in:

Obese:

7.3 ± 10.9 mg/24 h

Controls:

5.9 ± 5.7 mg/24 h,

p = 0.105

=

“Case2” control studies:

       

Pacifico, 2016 [25]

“Case” control

924

3–17

Hospital outpatient Clinics and from randomly selected local schools, Italy

Obesity, using WHO growth data

Microalbuminuria defined as ≥ 30 mg/24 h and < 300 mg/24 h

Outcome measure: Comparison of prevalence of microalbuminuria

Prevalence of microalbuminuria:

Obese: 13 (4.0%)

Normal: 0 (0%),

p < 0.0001

+

Franchini, 2015 [27]

318

6–18

Obesity Clinic, Diabetic clinic and from admissions to paediatric ward for minor trauma, fractures, Italy

BMI z-score

Log Albumin Excretion Rate (log AER)

Log AER adjusted for age, sex, and pubertal stage:

+

“Case” control

Microalbuminuria defined as AER = 20–200 μg/min

Obese = 0.978 ± 0.022

 

Outcome measures: Comparison of adjusted mean log AER

Controls = 0.852 ± 0.024,

 

Multivariate linear regression coefficient

p < 0.001

 

Regression coefficient for BMI z-score adjusted for age, sex and pubertal stage:

 

β = 0.135, p = 0.017

+

Bartz, 2015 [26]

58

14–16

Medical centre and through media advertising, USA

BMI category and response to oral glucose tolerance test (OGT)

Urine microalbumin excretion (UACR) (mg/g)

Urine microalbumin excretion in:

 

“Case” control study3

Outcome measures: Comparison of mean UACR

Normal BMI group:

 

Correlation coefficient

4.15 ± 0.53

 

Overweight and normal OGT:

 

6.03 ± 0.75

 

Overweight and pre_DM:

 

5.74 ± 0.98,

 

p > 0.05

=

Percentage body fat

Non-significant correlation between lnUACR and percentage body fat in multivariate model

=

Falakaflaki, 2012 [28]

“Case” control study

92

7–12

School children, matched on age and sex, Iran

BMI categories based on CDC growth data [21]

Albumin/creatinine ratio (ACR)

Outcome measure: Comparison of mean ACR

Albumin/creatinine ratio in:

Overweight:

0.049+/− 0.04

Normal:

0.043± 0.03,

p = 0.74

=

Savino, 2011 [29]

157

5–17

Hospital Obesity clinic, and from admissions to paediatric ward with minor illnesses, once recovered, Italy.

BMI SD categories: Obese= > 2SD for age and sex.

Urine albumin excretion rate (AER) (μg/min)

Urine AER in:

 

“Case” control study

Normal: within 2 SD of the 50th centile

Outcome measure: Comparison of mean AER

Obese:

10.2 ± 9.5

 

Correlation coefficient

  

Non-Obese:

 

7.5 ± 1.9,

 

p = 0.010

+

Correlation coefficient

 

BMI and AER: r = 0.301, p = 0.0001

+

Csernus, 2005 [44]

165

8–17

Obesity clinic, Hungary

BMI

UACR (mg/g)

Median UACR in:

 

“Case” control study

Obesity defined as a ratio between actual body weight and the ideal body weight for age, gender and height > 1.2

Outcome measures: Comparison of median UACR

Obese = 11.7 mg/g, interquartile range 12.9 mg/g

 

(corresponding to overweight or obesity by WHO definition)

Correlation coefficient

Normal = 9.0 mg/g, interquartile range 5.1 mg/g,

 

p = 0.003

+

Correlation between BMI v UACR:

 

r = 0.22, p < 0.05

+

“Case4” series

       

Xiao, 2014 [30]

“Case” series

242

≤ 19

Consecutive cases pre- bariatric surgery at each of five Teen-LABS centres, USA

BMI

Microalbuminuria defined as UACR ≥ 30 mg/g < 300 mg/g

Outcome measure:

Multivariate linear regression coefficient

No association between BMI and UACR in model adjusted for sex, age, race, DM, hypertension, HOMA-IR, HbA1c, ferritin, serum albumin, transferrin, dislipaedaemia, kidney stones

=

Radhakishun, 2013 [45]

408

3–19

Paediatric Obesity clinic

BMI z score

Microalbuminuria defined as UACR 2.5–25.0 mg/mmol in boys, 3.5–25.0 mg/mmol in girls

BMI z score:

 

“Case” series

Outcome measure: Comparison of mean BMIz score

Microalbuminuria group

3.6 ± 0.7

 

Outcome measure: Comparison of mean BMIz score

  

Adjusted odds ratio for microalbuminuria

Normo-albuminuria group:

 

3.2 ± 0.6,

 

p = 0.047

+

BMI z score OR for microalbuminuria adjusted by sex, pubertal stage and ethnicity not given, p = 0.054

=

Lurbe, 2013 [46]

“Case” series

134

9–18

Paediatric Obesity clinic

BMI z score based on WHO growth data

Overweight:

1 to < 2.0 SDS

Moderately Obese:

2.0–2.5 SDS

Severely Obese:

> 2.5 SDS

of 2.0 or more, namely, moderate obesity as a z score

Microalbuminuria defined as UACR ≥ 30 mg/g or in the “high-normal range” (UACR: 15–29 mg/g)

Outcome measure:

Correlation coefficient

Correlation coefficient BMI z score and log UACR:

r = 0.24, p < 0.05

+

Sanad, 2011 [47]

“Case” series

150

2–11

Paediatric Obesity clinic, Egypt

BMI

Microalbuminuria defined as UACR ≥30 mg/g and < 300 mg/g

Outcome measure:

Adjusted odds ratio for microalbuminuria

BMI OR for microalbuminuria adjusted for age, gender, abdominal obesity, hypertension, triglycerides, LDL, HDL insulin resistance and metabolic syndrome:

1.15 (1.02–4.55) p < 0.01

+

Burgert, 2006 [48]

“Case” series

277

12–15

Obesity clinic

USA

BMI z score

Microalbuminuria defined as UACR between 2 mg/mmol and 20 mg/mmol

Outcome measure: Comparison on mean BMI z score

BMI z score in:

Normal UACR group:

2.46 (2.41, 2.49)

Microalbuminuria group:

2.39 (2.27, 2.51),

p = 0.42

=

BMI body mass index, CDC Centers for Disease Control and Prevention, DM diabetes mellitus, HDL high-density lipoprotein, HOMA-IR homeostatic model assessment of insulin resistance, LDL low-density lipoprotein, OR odds ratio, SD standard deviation, UACR urine albumin-to-creatine ratio

* = Some studies may have been designed to assess another primary outcome, however in this table we have listed the renal outcome of interest for this systematic review

1 = this study was a prospective cohort but the data on BMI and albuminuria was analysed cross-sectionally

2 = cases defined by exposure = obesity

3 = cases defined by exposure, group 1 = overweight and normal oral glucose test (OGT), group 2 = overweight and pre-diabetes (impaired fasting glucose or impaired glucose tolerance)

4 = cases defined by exposure = obesity

Problems of measuring obesity and kidney function in the paediatric age

The association has been measured most often either as the difference in mean eGFR, which involves creatinine and less often cystatin C concentrations or level of albuminuria or microalbuminuria in obese children compared to children with normal weight for age and sex. Cystatin C-based eGFR are believed to be less influenced than creatinine by changes in muscle mass [49]. In a meta-analysis of studies comparing creatinine- to cystatin C-based eGFR in adults and children, most of whom had some form of kidney disease, Roos et al. [50] concluded that cystatin C was more accurate.

The most commonly used measure for obesity is body mass index (BMI), with obesity and overweight classified according to World Health Organization (WHO) growth data http://www.who.int/childgrowth/en/. Other measures such as waist circumference and waist to height ratio have also been used. Associations have been measured using either simple or adjusted correlation coefficients, with the measure of obesity and either eGFR or level of microalbuminuria as continuous variables. These factors make the interpretation of the findings difficult. Differences in the findings may be explained further by differences in the range of BMI studied, age and sex of participants, age of onset of obesity and its duration, ethnicity, diet and other confounders as well as by differences in the choice of substrate and equations to estimate GFR and by differences in the conditions of measurements of urine albumin (e.g. diurnal effects, exercise, degree of hydration, timed versus spot samples, single or multiple urine sampling). In a large population-based study involving 4305 children aged 6.0 years, Miliku et al. [51] showed that BMI was not associated with creatinine-based eGFR and that BMI was negatively associated with cystatin C-based eGFR. The effect was relatively small (− 0.61 ml/min/1.73 m2, 95% CI – 0.96 to − 0.26 per unit increase in BMI standard deviation score (SDS), p < 0.01). However, they showed that creatinine-based eGFR but not cystatin C-based GFR was influenced by fat mass (2.68 ml/min/1.73 m2, 95% CI 2.14 to 3.21 per 1 SD increase in fat mass percentage, p < 0.001) and by lean mass percentage (− 2.74 ml/min/1.73 m2, 95% CI – 3.27 to − 2.20 per unit increase in lean mass percentage, p < 0.001).

Which statistical association answers the question?

In the search for “independent” effects of obesity, many studies present measures of effect adjusted for risk factors which may be on the causal pathway between obesity and renal function, for example blood pressure, insulin resistance, abdominal obesity, HbA1c. For epidemiologists the question is: Is Obesity a risk factor for CKD? This means that what is of most importance from a public health perspective is the overall effect of obesity, either acting through direct nephrotoxic effects or acting indirectly on the kidneys by its effects in raising blood pressure or causing diabetes, with their ensuing effects on the kidneys. From this perspective, the overall effect of obesity needs to take into account age, sex, smoking status, socio-economic status and possibly ethnicity, as the key confounders. Therefore, many of these studies are over-adjusted.

Main findings of studies to date

Table 1 summarises the findings of studies on eGFR in relation to obesity. The cross-sectional population-based studies found a mix of positive, negative and no associations for eGFR with BMI, whilst for children from obese clinic populations compared to controls, there seems to be more of a consistent positive association between eGFR and BMI. The study by Pacifico et al. [25] showed that obese children were more likely than children with normal BMI to have a low eGFR, as well as being more likely than children with normal BMI to hyperfiltrate. These findings might possibly be explained by a changing effect of obesity on eGFR depending on the duration of obesity. This is an important factor which has not been measured in this study, or in any of the other listed studies, as most of these only assess overweight or obesity at a single time-point.

Most population-based studies on microalbuminuria have shown either no association with obesity or higher microalbuminuria in children with normal BMI (Table 2). The reason for this is not clear and some authors have suggested that the finding of microalbuminuria in non-obese children and adolescents might be confounded by orthostatic proteinuria. However, microalbuminuria has been found in obese children in studies based on obesity clinics, possibly linked to higher blood pressure.

Paediatric obesity and renal allograft function

The situation in which a recipient receives a new kidney is a unique model in which the influence of recipient-specific factors such as obesity on the transplant kidney function can be prospectively explored. There are a number of retrospective studies on the effect of paediatric obesity on subsequent renal allograft function during childhood. Obesity is common in children receiving a renal transplant and the first 4 months post-transplantation have been found to be a particularly sensitive period for weight gain [52]. This is thought to be at least in part mediated by steroid immunosuppressive therapy [53].

In a retrospective review of the records of 76 renal allograft recipients from 1994 to 2000, Mitsnefes et al. [54] found pre-transplant obesity to be associated with decreased allograft function after renal transplantation. Paediatric patients who were obese (BMI ≥ 95th percentile) both prior to transplant and 1 year post-transplant had lower mean directly measured GFR than both those who were non-obese prior to transplant but became obese by 1 year post-transplant (46.1 ± 15.0 mL/min/1.73 m2 vs. 57.7 ± 24.5 mL/min/1.73 m2, p < 0.05) and those who were non-obese prior to transplant and 1 year post-transplant (46.1 ± 15.0 mL/min/1.73 m2 vs. 60.4 ± 21.5 mL/min/1.73 m2, p < 0.01). The impact of paediatric obesity on long-term outcomes in paediatric patients with renal transplant was studied in an analysis of data from the North American Paediatric Renal Transplant Cooperative on 6658 paediatric age recipients. This showed that there had been an increase in the proportion of obese children from 8.2% in transplant recipients during the years 1987–1995 to 12.4% in children transplanted during the years 1996–2002 [55]. This study found no overall difference in patient mortality or graft survival between obese and non-obese children. However, in a post-hoc subgroup analysis it found a greater risk for death in obese children than non-obese children aged 6 to 12 years, and greater risk of living donor graft loss in obese than non-obese children aged 13–17 years. A study of 197 children from Brazil found decreased graft survival at 36 months post-transplant in children who had had an increase equal to or greater than 1 standard deviation in BMI between 1 and 6 months post-transplant [53]. In a more recent study of 750 renal transplant recipients in Australia, aged 2 to 18 years and with a median follow-up of 8.4 years, obesity was associated with a 60% greater risk of graft loss (hazard ratio (HR) 1.61, 95% CI 1.05–2.47) compared to children with normal BMI. There was no association between BMI and acute allograft rejection [56].

In summary, there are some data from the paediatric transplant population suggesting that obesity of the transplant recipient may be associated with accelerated graft loss.

Effects of bariatric surgery on renal function in paediatric age patients

Another way of looking at this question is to investigate the effect of weight reduction through bariatric surgery performed in children and adolescents on their subsequent kidney function. In a case report published in 2009, Fowler et al. [57] described the resolution of proteinuria resistant to medical treatment after bariatric surgery on a 15-year-old girl. This was associated with a reduction in her BMI from 56.8 to 32 kg/m2 post-surgery. The impact of bariatric surgery on renal function was described in a cohort of 242 severely obese adolescents [58] with a mean age of 17 ± 1.6 years, 29% of whom were aged 13–15 years. Post bariatric surgery there was an average increase in cystatin C-based eGFR of 3.9 (95% CI 2.4–5.4) ml/min/1.73 m2 per 10 kg/m2 decrease in BMI (p < 0.0001). The majority of improvement in renal function occurred in those with a baseline eGFR of < 90 ml/min/1.73 m2, whose mean (+/-SD) baseline eGFR was 76 ± 12 ml/min/1.73 m2. In this group, eGFR improved by 34% to 102 ± 28 ml/min/1.73 m2 at the 3-year follow-up evaluation (p < 0.0001). Amongst children with albuminuria pre-surgery, there was a fall in the geometric mean from a pre-surgery level of 74 mg/g (95% CI 45–121 mg/g) to 17 mg/g (95% CI 10–28 mg/g) at 3 years (p < 0.0001) post bariatric surgery. In 69% of children albuminuria normalised by the 2-year follow-up evaluation, and in 75% albuminuria resolved by 3-years post bariatric surgery. This study did not have a comparator group, there were no standardised selection criteria for surgery, and in addition, 24–29% of the 3 year follow-up data were missing. It is important to recognise that bariatric surgery is often followed by a change in diet, and if proteinuria was related to high blood pressure in those sensitive to salt, then it may not have been the surgery itself driving those changes in proteinuria over time. The evidence from studies in adults has already been mentioned [12, 13]. There have been no randomised controlled trials in adults and many of the studies are underpowered, and potential confounding by factors such as those mentioned remains a possibility.

What do epidemiological studies in the paediatric age show?

In summary, there is clear evidence that childhood obesity is associated with cardiovascular risk factors in adulthood, and that obesity in adulthood is associated with markers of incident CKD. The data are equivocal about whether an association between childhood obesity and renal function markers in childhood exists amongst children of the general population, whilst in clinic populations of referred obese children, there appears to be a subgroup of children with albuminuria and reduced eGFR. It may be that associations are difficult to detect depending on the timing of when the child was overweight and became obese, and how kidney function was measured (bearing in mind the limitations of creatinine-based equations). In contrast, the evidence from the paediatric renal transplantation population is very suggestive in that those who are most obese have accelerated graft loss. In addition, there are suggestive cross-sectional data on children aged between 5 and 19 years with eGFR of 30–90 mL/min/1.73 m2 and a diagnosis of CKD participating in the prospective cohort “Chronic Kidney Disease in Children” (CKiD) [59]. Children were divided into non-glomerular (n = 513) and glomerular (n = 224) forms of CKD. Using univariate linear regression, in the non-glomerular group BMI was not related to either urinary protein or eGFR, but waist circumference (WC) was associated with an increase in urinary protein of 7.51% (95% CI 0.45–15.06, p < 0.05) and no association with eGFR; in the glomerular group, BMI and WC were related to eGFR (BMI: 6.81%, 95% CI 2.67–11.12%, p < 0.0; WC: by 4.34%, 95% CI 0.00–8.88%, p < 0.05), but neither BMI nor WC were related to urinary protein. The main aim of the study was a comparison between BMI and WC as measures of obesity in this group of patients with established CKD and the conclusion was that WC added limited information to BMI in this cohort. However, these cross-sectional data suggest that the association of obesity with renal markers amongst children with CKD may depend on the type of underlying renal damage.

Epidemiological studies of the association of childhood obesity with adulthood kidney function

There have been a number of studies of risk factors with onset in childhood for CKD in adult life, either using end-stage renal disease (ESRD) or laboratory measurements to confirm CKD.

Muntner et al. [60] reported on 15 cases of ESRD in the Bogalusa cohort. Seven of the cases of ESRD had an unknown aetiology, and amongst other features, they had a higher BMI recorded in childhood compared to their counterparts. This study was of limited scope given the weakness in the ascertainment of cases of ESRD and absence of detailed follow-up data from childhood to the time when ESRD occurred.

Vivante et al. used a sample of 1.2 million Israelis examined prior to their military conscription at age 17 years in the period 1967–1997, linked to the Israeli ESRD registry for the period 1980–2010 [61]. In an adjusted model, overweight (BMI between 85th and 95th centile) and obesity (BMI ≥ 95th centile) at age 17 years were both strongly associated with all-cause treated ESRD (HR 3.00, 95% CI: 2.50–3.60 and 6.89, 95% CI: 5.52–8.59, respectively). The authors could not adjust for confounders such as socioeconomic status or smoking.

A third, carefully designed matched case-control study [62] examined the incidence of ESRD up to the age of 40 years in relation to risk factors measured in adolescence in males only using data collected at conscription to the Swedish military service between 1970 and 1975 when most were aged 18 or 19 years of age. The National Patient Register was used to ascertain data on new cases of ESRD from 1985, at least 10 years after the conscription examinations, to reduce confounding by undiagnosed kidney disease in adolescence. There were 534 incident ESRD diagnoses and 5127 men were selected as controls, representing the most powerful study to date. Elevated BMI at conscription was associated with increased ESRD risk with an adjusted odds ratio (OR) of 3.53 (95% CI 2.04–6.11, p < 0.001) for BMI > 30 kg/m2 compared to those with normal BMI (18.5–25 kg/m2). The odds for ESRD were also increased but to a lesser extent in those who had been overweight (BMI at conscription between 25 and 30 kg/m2), with an adjusted OR of 2.36 (1.79–3.13, p < 0.001) raising the possibility of a dose effect by BMI. The possible confounding effects of social status, and smoking, and the role of the subsequent BMI trajectory post conscription or current BMI were not examined.

In a nationwide population-based case-control study using incident cases of CKD as defined by laboratory test results [63], overweight (BMI > 25 kg/m2) at age 20 was associated with increased odds for CKD in middle-aged men and women, relative to BMI < 25 kg/m2: OR (adjusted for age, education, smoking, alcohol, and use of paracetamol and salicylates) = 3.1 (95% CI 2.1 to 4.8) in males, and 3.0 (95% CI 1.4 to 6.1) in females. The problem with this and other health record database studies using routine creatinine tests to examine this question is that people with BMI have more cardiovascular risk factors and therefore get tested earlier for presence/absence of kidney disease, which introduces information bias in favour of finding an association between BMI and incident CKD. As not everybody was tested the same way, no definitive conclusions can be drawn from this or similar routine health care studies.

To our knowledge the Medical Research Council National Survey of Health and Development (NSHD), a socially stratified sample of 5362 singleton children born in 1 week in March 1946 in England, Scotland and Wales, was the first study to systematically examine the effect of overweight throughout childhood and adulthood on later kidney function in a general population cohort [64, 65]. Repeated measurements of BMI between ages 2 and 20 years were used to derive childhood overweight latent classes (‘never’, ‘pre-pubertal only’, ‘pubertal onset’ or ‘always’), which were then related to measures of CKD at age 60–64 years: creatinine- or cystatin C-based eGFR < 60 ml/min/1.73 m2 or urine albumin-creatinine ratio (uACR) ≥ 3.5 mg/mmol. Relative to being in the never overweight latent class, being in the pubertal onset or always childhood overweight latent classes was associated with CKD at age 60–64 years when considering cystatin C-based eGFR (OR 2.04, 95% CI 1.09 to 3.82). The associations with CKD at age 60–64 years defined by creatinine-based eGFR (OR 1.27, 95% CI 0.71 to 2.29) and uACR (OR 1.33, 95% CI 0.70 to 2.54) were less marked but in the same direction. Adjustment for social class, lifestyle and health factors (e.g. smoking) had little impact on the effect estimates. The low prevalence of CKD (resulting in low statistical power) and the lack of repeated measurements of creatinine and cystatin C, meaning that the CKD chronicity criterion was not met, were limitations of the study [66].

Overall, there is some suggestive evidence from the NSHD cohort study, but otherwise, studies may be affected by the co-occurrence of social deprivation and smoking.

Mechanisms

The mechanisms relating childhood obesity to later CKD are not well researched [67], which is perhaps unsurprising considering that a follow-up over several decades may be needed to detect associations.

From studies conducted in animals and adults, there is evidence that obesity can lead to inflammation, oxidative stress, abnormal lipid metabolism, activation of the renin-angiotensin-aldosterone system, and increased production of insulin and insulin resistance. These abnormalities could lead to haemodynamic abnormalities, lipotoxicity and inflammation in the kidneys constituting the main mechanisms for obesity-related renal damage [68]. It is thought that the most important mechanism might be haemodynamic abnormalities affecting the renal microvasculature leading to hyperfiltration, development of proteinuria and ultimately to glomerulosclerosis and a reduction in GFR [69, 70, 71]. Studies in young people include a study in young men (mean age 18.4 years), where hyperfiltration was associated with overweight, elevated blood pressure, and low high-density lipoprotein cholesterol with multivariate-adjusted OR of glomerular hyperfiltration of 6.6 (95% CI 3.8–11.6), 1.8 (95% CI 1.0–3.0) and 2.5 (95% CI 1.5–4.3), respectively [72]. Franchini et al. [27] found that the prevalence of microalbuminuria and hyperfiltration was similar between obese and youths with Type 1 diabetes and higher than their control peers (6.0 vs. 8.0 vs. 0%, p = 0.02; 15.9 vs. 15.9 vs. 4.3%, p = 0.03, respectively). Possible mechanisms mediating paediatric kidney injury are thought to include inappropriate activation of the renal-angiotensin-aldosterone system, the inflammatory role of adipokines secreted by fat tissue such as leptin and adiponectin, renal effects of dyslipidaemia and insulin resistance [73, 74, 75].

Children who are overweight are at a higher risk of developing high blood pressure and diabetes, though there is a lack of evidence for effects independent of adulthood overweight [8]. A further study using NHSD data investigated the mediatory roles of diabetes, hypertension and obesity in the relationship between low birth weight and later renal function [76], and found that the observed association between low birth weight and low eGFR was not confounded by socioeconomic position and was not explained by diabetes or hypertension. There was some evidence that it was stronger in study members who were overweight in adulthood. These findings thus again suggested that overweight/obesity may accelerate kidney function loss in the presence of other kidney problems.

Challenges and future research directions

Although there is a plethora of studies looking at childhood obesity and renal function, they do not provide enough evidence for a direct causal pathway between childhood obesity and adult CKD. The evidence that obesity is associated negatively with renal function in the paediatric age is not conclusive, and it may be instead that obesity may accelerate renal function loss in the context of other (subclinical) renal damage.

One deficiency in research to date has been an inability to distinguish whether the effects of childhood obesity on CKD are due to childhood obesity in and of itself (i.e. that childhood is a ‘critical period’ or a ‘sensitive period’ [77]), or whether the observed effect is merely due to childhood obesity being indicative of greater lifelong exposure to obesity pressure (i.e. an ‘accumulation of risk’ [77]). In addition, to date, authors have not fully explored the role of other factors that co-occur with obesity, such as a bad diet with high calories and salt, leading to both obesity and high blood pressure. The challenge of research investigating the question of whether childhood obesity causes kidney damage independently of adulthood obesity (‘sensitive period’) is the statistical necessity to have sufficient subjects who become normal weight in adulthood after being obese in childhood to make analysis tenable, and the fact that reasons for weight loss and the way weight loss occurs may itself impact on kidney health. There were very few subjects in the NSHD data who lost weight, which meant that data were not available to address this important question [65].

The number of years lived with adulthood obesity are known to be directly associated with the risk of all-cause and cause-specific mortality [78]. Since the obesity-related metabolic, oxidative and inflammatory damage to many tissues and organs may accumulate over time [79], kidney dysfunction resulting from lifelong obesity beginning in childhood would not be unexpected. To date, we have not seen evidence that CKD prevalence or ESRD incidence increase as a result of increasing prevalence of childhood obesity. However, based on the data from the NHSD, the lag time between childhood overweight and adult CKD may well be several decades [64, 65].

It is unclear whether childhood obesity causes pre-adulthood kidney damage. A further investigation of this question would require repeated follow up of very sensitive kidney function markers in a prospective birth cohort study. In general population cohorts, this necessitates large sample sizes. Such a study should carefully characterise nutrition and physical activity to better understand which of the pathways that lead to obesity may have most impact on kidney health.

The finding that obesity strengthens the association of low birth weight with later poorer kidney function is particularly worrying in the setting of low middle income countries which are undergoing a rapid rate of urbanisation with an associated rise in obesity [80]. Indeed, in the Indian Migrant study, the years lived in urban settings were associated with decreased eGFR amongst rural-urban migrants in age-adjusted analyses [81].

Conclusions

The association between adulthood obesity and kidney disease is well-documented, but the evidence for a similar effect of childhood obesity is patchy. Although there remains much research to be done in this area, in view of the overall impact of childhood obesity on health it is becoming apparent that efforts to prevent and treat obesity early in life could potentially have an impact on the progression, costs and comorbidities of kidney disease [75]. It is thought that reducing the amount of time that individuals live with obesity (“obesity-years”) by preventing early onset may be critical in minimising a variety of long-term hazards of obesity on health [79].

Notes

Compliance with ethical standards

Support and financial disclosure declaration

This work was supported by Kidney Research UK (grant number RP34/2009) to RJS and DN.

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© IPNA 2018

Authors and Affiliations

  1. 1.Department of PaediatricsGloucestershire Royal HospitalGloucesterUK
  2. 2.Department of Medical Statistics, Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonUK
  3. 3.Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonUK

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