Heavy Metals and Pesticides in Chronic Kidney Disease - Results from a Matched Case-Control Study from a Rural Population in Shivamogga District in South India

Abstract

Introduction:
There is a high prevalence of chronic kidney disease (CKD) in the rural agrarian population of South India and it often appears unrelated to major known causes such as diabetes or glomerulonephritis.

Methods:
In a matched case-control study conducted in a rural population in Shivamogga district in South India, the association of heavy metals - lead (Pb), arsenic (As), cadmium (Cd) - and pesticides in CKD was studied. Blood and spot urine samples were tested quantitatively for heavy metals and qualitatively for pesticides.

Results:
In all, 69 matched pairs (40 female, 58%) were recruited. The mean estimated glomerular filtration rate (mL/min/1.73 m2) was 60.1 (14.2) in cases and 83.4 (13.4) in controls. Elevated blood lead level >5 μg/dL was seen in 15 cases and 25 controls, respectively [P = 0.035, matched odds ratio (MOR) 0.5, 95% confidence interval (CI) 0.22-1.05]. Urinary Pb was elevated in 16 cases and 13 controls, respectively (P = 0.28, MOR 1.25, 95% CI 0.58-2.73). There was no significant association with As and Cd, while pesticide residues were undetectable in cases as well as controls. These results did not change even after excluding CKD cases with diabetes, stage 2 hypertension, and significant proteinuria.

Conclusions:
There was no statistical significant association between any of the studied heavy metals and CKD, although there was a significant burden of heavy metals in the studied subjects.

Full text

402 © 2019 Indian Journal of Nephrology | Published by Wolters Kluwer - Medknow
Introduction
Chronic kidney disease (CKD) is widely
acknowledged to be a global epidemic.[1]
The global prevalence is on the rise and is
estimated to be between 11% and 13%.[2]
The rise in prevalence is not only seen in
the developed countries but also seen in
the developing countries. The cause for this
appears to be multifactorial. The changing
lifestyle among the people in these
countries due to socioeconomic transition
and the ageing trend of the population
have led to an increase in comorbidities
such as diabetes, hypertension, obesity, and
cardiovascular diseases that have directly
contributed to the CKD burden.[3] Factors
such as poor sanitation, overcrowding,
illiteracy, infections, low socioeconomic
status, and poor reach of quality medical
services to remote areas have also resulted
in an increase in the CKD prevalence
Address for correspondence:
Dr. Y. J. Anupama,
Nanjappa Hospital,
Shivamogga - 577 201,
Karnataka, India.
E-mail: anupamayj@gmail.com
Access this article online
Website: www.indianjnephrol.org
DOI: 10.4103/ijn.IJN_325_18
Quick Response Code:
Abstract
Introduction: There is a high prevalence of chronic kidney disease (CKD) in the rural agrarian
population of South India and it often appears unrelated to major known causes such as diabetes
or glomerulonephritis. Methods: In a matched case–control study conducted in a rural population
in Shivamogga district in South India, the association of heavy metals – lead (Pb), arsenic (As),
cadmium (Cd) – and pesticides in CKD was studied. Blood and spot urine samples were tested
quantitatively for heavy metals and qualitatively for pesticides. Results: In all, 69 matched pairs
(40 female, 58%) were recruited. The mean estimated glomerular ltration rate (mL/min/1.73
m2) was 60.1 (14.2) in cases and 83.4 (13.4) in controls. Elevated blood lead level >5 μg/dL
was seen in 15 cases and 25 controls, respectively [P = 0.035, matched odds ratio (MOR) 0.5,
95% condence interval (CI) 0.22–1.05]. Urinary Pb was elevated in 16 cases and 13 controls,
respectively (P = 0.28, MOR 1.25, 95% CI 0.58–2.73). There was no signicant association
with As and Cd, while pesticide residues were undetectable in cases as well as controls. These
results did not change even after excluding CKD cases with diabetes, stage 2 hypertension, and
signicant proteinuria. Conclusions: There was no statistical signicant association between any
of the studied heavy metals and CKD, although there was a signicant burden of heavy metals in
the studied subjects.
Keywords: Chronic kidney disease, epidemiological study, heavy metals, India, matched
case–control study, rural population
Heavy Metals and Pesticides in Chronic Kidney Disease – Results from
a Matched Case–Control Study from a Rural Population in Shivamogga
District in South India
Original Article
Y. J. Anupama,
S. K. Kiran1,
Shrikanth N. Hegde2
Department of Nephrology,
Nanjappa Hospital, Shivamogga,
1Taluka Medical Ofcer,
Thirthahalli, 2Department of
Medicine, Anushri Medical
and Diabetes Care Center,
Shivamogga, Karnataka, India
How to cite this article: Anupama YJ, Kiran SK,
Hegde SN. Heavy metals and pesticides in chronic
kidney disease – Results from a matched case–
control study from a rural population in Shivamogga
district in South India. Indian J Nephrol 2019;29:402-9.
This is an open access journal, and arcles are
distributed under the terms of the Creave Commons
Aribuon‑NonCommercial‑ShareAlike 4.0 License, which
allows others to remix, tweak, and build upon the work
non‑commercially, as long as appropriate credit is given and
the new creaons are licensed under the idencal terms.
For reprints contact: reprints@medknow.com
in these countries.[4,5] CKD of unknown
etiology (CKDu) is a new disease entity
seen in some of the developing countries,
predominantly affecting the population
dependent on agriculture. Mesoamerican
nephropathy in the Central American
countries and Sri Lankan nephropathy in
Sri Lanka are the well‑described disease
prototypes.[6,7] They have in common a
chronic tubulointerstitial disease pattern
and are etiologically linked to toxins in the
environment.[8‑11] Heavy indiscriminate use
of agrochemicals with consequent toxic
contamination of the drinking water and
food with heavy metals and heat stress are
some of the causes incriminated.[12‑14]
In India, CKDu similar to these prototypes
has been described from the Uddanam
area of Andhra Pradesh.[15] However, it
may be more widespread than previously
believed. In a previous study done on the
rural population of Shivamogga district in
South India, more than 40% of the subjects
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Anupama, et al.: CKD and heavy metals
Indian Journal of Nephrology | Volume 29 | Issue 6 | November-December 2019 403
had minimal or no proteinuria and mild to moderate
hypertension which raised the possibility of CKDu.[16] In
a subsequent follow‑up conducted in 2014–2015 also, a
similar trend was observed.[17] A preliminary evaluation for
heavy metals revealed increased levels of urinary lead in
some of these subjects (author’s unpublished observations).
Hence, this study was undertaken to systematically evaluate
the levels in blood and urine for heavy metals such as
lead (Pb), arsenic (As), cadmium (Cd) in patients with
CKD and to analyze whether there was any association
between the two. The study also looked at the pesticide
residues and their association with CKD.
Methods
Study setting and study design
This matched case–control study was conducted in
2015–2016 on the adult population of Hosakoppa,
Indiranagar, and Gajanur villages of Shimoga district,
Karnataka state in South India. The villages are located
about 10 km from Shivamogga town, with agriculture
being an important occupation. The villages had been
screened on two previous occasions, in 2011–2012[16] and
again in 2014–2015.[17] All the persons who were identied
as patients with CKD in these previous studies were
approached for recruitment. The consenting individuals
were reevaluated and recruited as cases in this study, after
conrming the diagnosis of CKD. We recruited 69 cases
after excluding those subjects who were not available for
interview even after visits to their houses on two different
days and those subjects who had relocated or did not
consent to the study. A list of subjects who did not have
CKD as found in the earlier studies was prepared. Line
list of these subjects who were matched with cases for
age (+ or − 4 years), gender, and village was nalized.
Controls were selected from this line list using random
number tables generated by OpenEpi version 3.1.[18] If a
selected control had relocated, died, or was not available
for interview even after visits to his house on two different
days, the subject next in the list was selected as a matched
control.
Study population
All adults (age 18 years and above) were considered for
inclusion in the study. Elderly people >75 years of age,
pregnant women, and women in the postnatal period
(up to 6 weeks after delivery) were excluded. Cases
of CKD in stage 5 already receiving dialysis and renal
transplant recipients were also excluded.
Data collection and tools
The study was conducted from August 2015 to May
2016. The study was approved by the Institutional Ethics
Committee. A written, informed consent was taken from
each participant. The eld work was done by one of the
authors (SKK) along with the students of the nursing
college afliated to the investigating institution. All
participants were administered a structured questionnaire,
which included demographic details such as educational
and occupational status, living conditions, and personal
habits such as smoking and alcohol use. History of diabetes,
hypertension, renal disease, ischemic heart disease, stroke,
and arthritis was elicited. Details of current medications,
long‑standing analgesic use, and indigenous medicine intake
were as recorded. Weight, height, waist circumference, and
hip circumference were measured as per standard protocol.
Waist/hip ratio and body mass index were calculated. Blood
pressure (BP) measurements were taken for the entire group
using mercury sphygmomanometers (Diamond, Pune,
India), which were regularly calibrated. Two measurements
were taken in the sitting position 5 min apart with the arms
resting on a surface and the average was considered to be
the BP.
Blood samples were collected for the following tests:
hemoglobin, glycosylated hemoglobin (HbA1C), serum
creatinine, and uric acid. Blood samples were analyzed in
the parent institution using a fully automated biochemistry
analyzer (A‑25; Biosystems SA, Barcelona, Spain). HbA1C
was tested by high‑performance liquid chromatography
method using Bio‑Rad D10 analyzer (Bio‑Rad
Laboratories, Inc., Hercules, CA, USA). Serum creatinine
was analyzed by the Jaffe’s method using standards and
reagents (Biosystems SA) which are traceable to isotope
dilution mass spectroscopy. Uric acid was analyzed using
glucose oxidase method. Glomerular ltration rate (GFR)
was estimated in all subjects using the Modication of
Diet in Renal Diseases (MDRD) study equation. Random
midstream urine samples were assessed using 11‑parameter
dipstick (Agappe Diagnostics, Ernakulam, Kerala, India)
for urine protein, sugar, blood, and macroalbuminuria (MA)
mechanically read by urine analyzer (MispaUriskan 100;
Agappe Diagnostics, Ernakulam, Kerala, India). In all
cases, albuminuria was also examined by semi‑quantitative
estimation of urinary albumin creatinine ratio (ACR)
using Siemens Clinitek dipstick (Siemens Healthcare
GmBH, Erlangen, Germany). It was mechanically read by
BiosenseUchek, a smartphone‑based portable diagnostic
system (Biosense Diagnostics, Thane, Maharashtra, India).
Quantitative analysis of urinary Beta‑2 microglobulin (β‑2),
was done by immunoturbidimetric method using Beta‑2 m
turbilatex kit (Spin‑React, SA, Sant Esteve De Bas, Spain).
Care was taken to transport all blood and urine samples to
the laboratory in cold chain and process them immediately.
Toxicology analysis
Toxicological analysis was done in a national‑accredited
toxicology laboratory at Amrita Institute of Medical
Sciences, Cochin, India. Precautions were taken to avoid
contamination of the samples during collection and
transport. Blood and urine were analyzed quantitatively
for lead, arsenic, and cadmium by inductively coupled
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Anupama, et al.: CKD and heavy metals
404 Indian Journal of Nephrology | Volume 29 | Issue 6 | November-December 2019
plasma atomic energy spectrometry method (Iris Intrepid
II XSP Duo; Thermo Electron Corporation, Madison,
WI, USA). Samples were also tested qualitatively by
thin‑layer chromatography for residues of a panel of
commonly used pesticides. The tested compounds included
malathion, methyl parathion, quinalphos, monocrotophos,
chlorpyriphos, carbofuran, carbaryl, carbendazim, propoxur,
lindane, DDT, cypermethrin, permethrin, prallethrin, and
imidacloprid.
Denitions of relevant parameters in the study
The case of CKD was dened as an adult residing in the
villages mentioned above who had evidence of CKD.
CKD was dened as the presence of either kidney damage
or GFR <60 mL/min/1.73 m2.[19] Persistent albuminuria,
dened as urine positive for MA on dipstick and spot
ACR ≥30 mg/g, was taken as the indicator of kidney
damage. Proteinuria was detected by the presence of
protein in urine as indicated by 1+ (0.3 g/L) or more on
dipstick.[20] Adult residing in the above‑mentioned villages,
who had no evidence of CKD, was considered to be the
control.
Hypertension was dened as the presence of systolic
BP ≥140 mmHg and/or diastolic BP ≥90 mmHg
or self‑reported history of hypertension or use of
antihypertensive medications.[21] Diabetes mellitus was
dened as HbA1C ≥6.5% or self‑reported history of diabetes
or taking antidiabetes medications.[22] BMI was staged
using the World Health Organization Asia Pacic criteria:
malnutrition <18.5 kg/m2, normal BMI 18.5–22.9 kg/m2,
overweight >23 kg/m2, and obesity >25 kg/m2.[23] Abdominal
obesity was identied by the criteria of WC in men >90 cm
and women >80 cm.[23] Urinary β‑2 microglobulin levels
were considered elevated when the levels exceeded
0.3 µg/mL.[24]
Heavy metals and pesticides: Elevated blood lead level
(BLL) was dened as blood lead more than 5 µg/dL. Blood
cadmium levels >6 µg/L and blood arsenic level >12 µg/L
were considered elevated. Urinary lead, cadmium, and
arsenic levels were considered elevated when spot urine
lead, cadmium, and arsenic levels were more than 4, 1.3,
and 35 µg/L, respectively.[25]
Statistical analysis
Baseline descriptive data are presented as proportions,
mean with standard deviation, and median as applicable
to individual variables. Comparative data by groups are
described as mean and 95% condence intervals (CIs).
Student’s t‑test or Mann–Whitney U‑tests were used to
compare the variables between the two groups depending
on the normality of the data. SPSS version 16 (SPSS,
Inc., Chicago, IL, USA) was used to perform above
analysis. Concordance and discordance among pairs for
each variable of interest were manually identied and
entered into paired matched odds ratio (MOR) contingency
tables, using OpenEPI version 3.01 software, and mid
P exact values and MOR were obtained. Two‑sided P value
of <0.05 and MOR >1 with 95%condence intervals were
accepted as statistically signicant. For analyzing blood
and urine heavy metal levels, the values reported to be
below detectable limits were censored to calculate means
and medians. Pearson’s product moment correlation was
determined to assess correlation between heavy metals
and estimated GFR (eGFR) and also with urinary β‑2
microglobulin levels.
Results
Baseline characteristics
In all, 69 pairs of cases and controls were recruited. There
were 40 (58%) female pairs and 29 (42%) male pairs. The
mean [±SD] ages of cases and controls were 49.2 ± 13.8
and 48.9 ± 13.3 years, respectively. The sociodemographic
characteristics of cases and controls are summarized in
Table 1.
Analysis of cases and controls
Clinical characteristics of cases and controls are summarized
in Table 2. The mean serum creatinine was 1.15 ± 0.26 mg/
dl in cases and 0.85 ± 0.14 mg/dl in controls (P < 0.001).
The mean eGFR was 60.12 ± 14.28 ml/min in cases and
83.09 ± 12.97ml/min in controls (P < 0.001). Albuminuria
was seen in 47 (68%) and 6 (8.7%) had signicant
proteinuria. Stagewise distribution of the CKD cases was
as follows: stage 1: 2.9%, stage 2: 43%, stage 3a: 40%,
and stage 3b: 13%. There were no patients in stage 4 CKD.
One‑fourth of our study population were diabetics (cases:
n = 13, 19%; controls: n = 15, 21%, P = 0.81). In all,
59% of cases (n = 41) and 43% of controls (n = 30) were
found to be hypertensive (P = 0.03). The majority of the
study subjects had BMI in the normal range, while 23%
had BMI >25 kg/m2. Abdominal obesity was seen in 44%.
There were no signicant differences between the two
groups when means of variables were examined, apart
from serum creatinine and eGFR [Table 2]. However,
one important observation was that 27 participants had
signicant elevation of urinary β‑2 microglobulin, of whom
23 (85%) were cases and only 4 (15%) were controls. The
mean and median urinary β‑2 microglobulin in the cases
were 0.85 ± 1.32 and 0.13 µg/mL, respectively, while that
in controls was 0.09 ± 0.14 and 0.04 µg/mL, respectively
(P < 0.001).
Toxicological analysis
Blood lead and urinary lead levels were high in a signicant
number of study participants [Figures 1 and 2]. In all, 40%
of the participants (27% cases and 50% controls) had
elevated BLL [Table 3]. Nearly 75% of them had blood
lead above >5 µg/dL. Urine lead was elevated in 21% of
participants (23% cases and 19% controls). The median
BLL and ULL was 22.25 and 178 µg/L, respectively.
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Anupama, et al.: CKD and heavy metals
Indian Journal of Nephrology | Volume 29 | Issue 6 | November-December 2019 405
However, they did not differ signicantly between cases
and controls. A few of the subjects had blood and urine
cadmium and arsenic too. Urine arsenic was elevated in 11%
of the subjects (9% cases and 13% controls). The median
level of urine arsenic was signicantly higher in cases (501
µg/L) than in controls (233.5 µg/L; P = 0.04) [Figure 3].
However, there was no signicant association between
cases and controls with any of the heavy metals [Table 4].
Table 2: Comparison of anthropometric and clinical characteristics between cases and controls
Characteristic, mean (SD) Cases, n=69 Controls, n=69 P95% CI
Height, cm 155.4 (8.05) 156.7 (1.1) 0.374 −4.24, 1.60
Weight, kg 53.9 (4.2) 53.1 (12.0) 0.677 −3.01, 4.61
Body mass index, kg/m222.2 (4.3) 21.6 (3.76) 0.317 −0.66, 2.04
Hip circumference, cm 91.14 (10.43) 89.52 (8.34) 0.315 −1.55, 4.80
Waist circumference, cm 82.44 (12.25) 80.91 (8.34) 0.459 −2.55, 5.63
Waist/hip ratio 0.90 (0.076) 0.89 (0.079) 0.321 −4.22, 1.39
Systolic blood pressure, mmHg 135.72 (22.69) 130.58 (17.93) 0.106 −1.21, 12.55
Diastolic blood pressure, mmHg 84.72 (14.27) 83.13 (8.59) 0.428 −2.37, 5.56
Hemoglobin, g/dL 12.58 (2.62) 12.24 (1.91) 0.39 −0.43, 1.11
Glycosylated hemoglobin, % 5.94 (1.21) 5.83 (1.29) 0.612 −0.31, 0.53
Serum creatinine, mg/dL 1.15 (0.26) 0.85 (0.14) <0.001 0.22, 0.36
Serum uric acid, mg/dL 3.87 (1.15) 4.18 (1.13) 0.108 −0.70, 0.07
Estimated GFR, MDRD mL/min/1.73 m260.12 (14.28) 83.09 (12.97) <0.001 −27.56, −18.37
SD: Standard deviation; CI: Condence interval; GFR: Glomerular ltration rate; MDRD: Modication of Diet in Renal Diseases study
equation. Signicant difference marked in bold
Table 1: Sociodemographic characteristics of study population
Characteristic Cases, n=69 % Controls, n=69 %
Age (years), mean (SD) 49.2 (13.8) NA 49.04 (13.46) NA
Males, no. (%) 29 42.0 29 42.0
Education status ‑ elementary school 58 84.1 60 87.0
Occupation: agriculture 28 40.6 21 30.4
Occupation: manual labor 16 23.2 32 46.4
Nonvegetarian food habits 60 87.0 57 82.6
Smoking 15 21.7 6 8.7
Chewing of tobacco 19 27.5 13 18.8
Alcohol 31 44.9 25 36.2
Self‑reported diabetes 8 11.6 6 8.7
Self‑reported hypertension 24 34.8 10 14.5
Self‑reported history of renal stones 2 2.9 3 4.3
SD: Standard deviation; NA: Not applicable
Figure 2: Urine lead levels among cases and controlsFigure 1: Blood lead levels among cases and controls
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Anupama, et al.: CKD and heavy metals
406 Indian Journal of Nephrology | Volume 29 | Issue 6 | November-December 2019
Pearson's product moment correlation coefcients generated
for heavy metals with eGFR also suggest that there is a
positive correlation between the heavy metals but not with
eGFR [Table 5]. There was no signicant correlation of
studied heavy metal levels with urinary β‑2 microglobulins
as well [Table 6]. Pesticide residues were not detected in
either cases or controls by the method studied.
Subgroup analysis
We identied those cases without diabetes, signicant
hypertension (BP ≥160/110 mmHg), overt proteinuria
(>0.3 g/L), and self reported kidney stones and considered
them to be having CKD of undetermined etiology (CKDUD).
A pair matched analysis for all the variables including heavy
metals with their respective controls did not show signicant
association between the heavy metals and CKDUD.
Discussion
In this study, we found that our patients with CKD had
signicant urinary excretion of β‑2 microglobulin even when
they did not have signicant proteinuria or albuminuria. There
was signicant lead burden as demonstrated by high blood
and urine lead levels in the study population. A few subjects
also had high levels of arsenic and cadmium. However, in
our limited study, we could not demonstrate any signicant
positive association between these heavy metals and CKD.
Table 3: Heavy metals among cases and controls
Heavy metals Statistical measure Cases, n=69 Controls, n=69
Blood lead Detectable values, number 19 35
Range 4.4‑155.1 2.3‑195.1
Signicant (>5 µg/dL), number 15 25
Mean* (SD) 35.6 (39.3) 34.5 (40.9)
Median (IQR)* 24.7 (38.7) 19.5 (41.8)
P** 0.69
Urine lead,
µg/L
Detectable values, number 16 13
Range 24.2‑1200 24637
Mean* (SD) 342.9 (380.4) 234.7 (398.3)
Median (IQR)* 227.2 (467.2) 142.6 (398.3)
P** 0.661
Blood arsenic,
µg/L
Detectable values, number 3 2
Range# 101 101
Mean* 101 101
SD NA NA
Median (IQR)* NA NA
Urine arsenic,
µg/L
Detectable values, number 6 9
Range 448‑501 44.4‑440.6
Mean* (SD) 483.5 (27.1) 216.4 (136.4)
Median (IQR)* 501 (52.3) 233.5 (224)
P** 0.001
Blood
cadmium,
µg/L
Detectable values, number 1 6
Range 44.6 4.5‑69.2
Mean* (SD) NA 43.4 (21.2)
Median (IQR)* NA 46.5 (20.8)
Urine
cadmium,
µg/L
Detectable values, number 1 5
Range 54.5 25.9‑178.9
Mean* (SD) NA 67.7 (62.6)
Median (IQR)* NA 44.8 (77)
SD: Standard deviation; CI: Condence interval; IQR: Interquartile range; NA: not applicable. *Derived after censoring undetectable values;
**derived for difference between rank sum of the two groups; #all subjects had the same value; signicant difference indicated in bold
Figure 3: Urine arsenic among cases and controls
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Anupama, et al.: CKD and heavy metals
Indian Journal of Nephrology | Volume 29 | Issue 6 | November-December 2019 407
In India, CKDu has been described from the coastal belt of
Andhra Pradesh and Odisha states. Our study suggests that
CKDu may be more widespread in India than previously
thought. We had earlier reported a high prevalence of
nonproteinuric CKD in agricultural workers in a previous
study conducted in the same area in Karnataka state in
South India.[16] In this study too, 32% cases had urinary
ACR <30 mg/g. Furthermore, our cases had signicant
urinary excretion of β‑2 microglobulin. Increased urinary
excretion of low molecular weight proteins such as β‑2
microglobulin is a feature of most toxic nephropathies.[26,27]
It indicates proximal tubular dysfunction and has been
described in lead nephropathy and nephrotoxicity due to
cadmium, arsenic, and mercury.[28] Low‑molecular‑weight
proteinuria has also been described in CKDu as seen in
Sri Lanka and the Central American countries and it is
likely that our patients too have similar tubulointerstitial
pattern of kidney damage. However, in our limited sample,
we did not nd a signicant correlation of urinary β‑2
microglobulin with the studied heavy metal levels in either
blood or urine.
Studies in Sri Lanka point to a possible link between
environmental toxins and CKDu. Various studies have
demonstrated increased urinary excretion of As, Cd, and
glyphosate in cases with CKDu.[29‑31] The source of these
metals may be agrochemicals or other environmental sources
such as well water, rice, sh, and pesticides.[32‑35] However,
studies on possible environmental toxic exposures carried
out in the coastal belt of Andhra Pradesh in India have not
reported a positive association with either agrochemicals or
heavy metals till date.[36,37] In our study too, we could not
demonstrate signicant association between CKD and heavy
metals in either blood or urine. It is possible that we could
not demonstrate a positive association between the heavy
metal levels and CKD because our study patients had milder
renal disease compared with the subjects in the Sri Lankan
studies.[14,38] It is possible that the effect of these metals may
be better demonstrable as renal functions decline further.
Interactions between metals or genetic polymorphisms also
may inuence effect of the metals on the kidneys.[39,40]
To the best of our knowledge, this is a rst ever matched
case–control study from India that studied the association
of CKD with heavy metals and pesticides. Another positive
Table 5: Correlation between metals and with estimated GFR
Heavy metals Correlation
with metals
Pearson’s
correlation
Sig.
(two‑tailed)
Pearson’s correlation
with estimated GFR
Sig.
(two‑tailed)
Blood lead Urine lead 0.101 0.239 0.061 0.477
Blood arsenic 0.439** <0.001
Urine lead Blood cadmium 0.263** 0.002 −0.016 0.85
Urine cadmium 0.195* 0.022
Blood arsenic Urine lead −0.064 0.453 −0.105 0.221
Urine arsenic 0.484** <0.001
Urine arsenic Blood cadmium −0.041 0.631 −0.079 0.357
Urine cadmium −0.032 0.707
Blood cadmium Urine cadmium 0.521** <0.001 0.017 0.841
Urine cadmium Urine lead 0.195* 0.022 0.092 0.285
GFR: Glomerular ltration rate. **Correlation is signicant at the 0.01 level (two‑tailed); *correlation is signicant at the 0.05 level (two‑tailed);
signicant correlations denoted in bold
Table 6: Correlation of urinary beta‑2 microglobulins
with heavy metals and renal damage indicators
Variable Pearson’s correlation Signicance
Blood lead 0.12 0.892
Urine lead −0.083 0.381
Urine arsenic −0.112 0.192
Estimated GFR −0.384** <0.001
Urinary protein 0.259** 0.002
Microalbuminuria 0.306** <0.001
GFR: Glomerular ltration rate. **Correlation is signicant at the
0.01 level (two‑tailed); Please note: correlation not checked for
blood arsenic and cadmium as the positive samples are very few in
number; signicant correlations denoted in bold
Table 4: Association of heavy metals with chronic kidney disease
Heavy metals Cases, no. (n=69) % Controls, no. (n=69) % PMOR 95% CI
Blood lead >5 mcg/dL 15 21.74 25 36.23 0.061 0.47 0.20, 1.03
Urine lead 16 23.19 13 18.84 0.571 1.25 0.58, 2.73
Blood arsenic 3 4.35 2 2.90 >0.991 1.50 0.22, 12.61
Urine arsenic 6 8.70 9 13.04 0.424 0.62 0.18, 1.93
Blood cadmium 1 1.45 6 8.70 0.071 0.16 0.01, 1.12
Urine cadmium 1 1.45 5 7.25 0.125 0.20 0.01, 1.44
MOR: Matched odds ratio; CI: Condence interval
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Anupama, et al.: CKD and heavy metals
408 Indian Journal of Nephrology | Volume 29 | Issue 6 | November-December 2019
aspect of our study is the nding of signicant urinary
excretion of β‑2 microglobulin in CKD cases that probably
indicates a tubulointerstitial pattern of kidney damage. This
is another nding that has not been demonstrated in any
Indian study on CKD till date.
The high lead burden in the study subjects is alarming. Our
study showed that nearly 40% of the study participants
have elevated BLLs. Spot urine lead was elevated in
about 20% of the participants. The magnitude of the blood
and urine lead levels is large and far exceeds the range
of lead levels seen in those studies which were done on
occupationally lead‑exposed subjects. This has enormous
public health implications and needs further preventive
measures.
There are some limitations with our study. It would have
been more accurate to report urinary heavy metal levels
corrected to urinary creatinine values which we did not do
in our analysis. A few Sri Lankan studies have reported
higher uncorrected urinary heavy metal levels in controls
than in cases, but when the levels were corrected to urinary
creatinine, the levels were signicantly higher in cases than
in controls.[14,38] It would have been desirable to test the
subjects for pesticide residues using quantitative methods.
Third, the small sample size limits the generalizability of
the ndings and calls for larger study in this direction.
In summary, this study showed that there is no demonstrable
association between CKD and heavy metals in this part
of rural area of Shivamogga district in South India.
A signicant lead burden among the study participants and
high levels of arsenic and cadmium in a few participants
have been detected. This calls for more studies in this area
to identify the source of heavy metals.
Acknowledgements
The authors thank the management, Nanjappa Hospital,
Shivamogga, Karnataka, India, and the staff of Nanjappa
Hospital and Nanjappa Institute of Nursing Sciences for
logistic support.
Financial support and sponsorship
The authors thank the International Society of Nephrology
(Clinical Research Grant #05‑038), Brussels, Belgium, for
the nancial grant for the project.
Conicts of interest
There are no conicts of interest.
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