Abstract

Introduction

   Sugarcane workers are exposed to potentially hazardous agrochemicals,
   including pesticides, heavy metals, and silica. Such occupational
   exposures present health risks and have been implicated in a high rate
   of kidney disease seen in these workers.

Methods

   To investigate potential biomarkers and mechanisms that could explain
   chronic kidney disease (CKD) among this worker population, paired urine
   samples were collected from sugarcane cutters at the beginning and end
   of a harvest season in Guatemala. Workers were then separated into 2
   groups, namely those with or without kidney function decline (KFD)
   across the harvest season. Urine samples from these 2 groups underwent
   elemental analysis and untargeted metabolomics.

Results

   Urine profiles demonstrated increases in silicon, certain pesticides,
   and phosphorus levels in all workers, whereas heavy metals remained
   low. The KFD group had a reduction in estimated glomerular filtration
   rate (eGFR) across the harvest season; however, kidney injury marker 1
   did not significantly change. Cross-harvest metabolomic analysis found
   trends of fatty acid accumulation, perturbed amino acid metabolism,
   presence of pesticides, and other known signs of impaired kidney
   function.

Conclusion

   Silica and certain pesticides were significantly elevated in the urine
   of sugarcane workers with or without KFD. Future work should determine
   whether long-term occupational exposure to silica and pesticides across
   multiple seasons contributes to CKD in these workers. Overall, these
   results confirmed that multiple exposures are occurring in sugarcane
   workers and may provide insight into early warning signs of kidney
   injury and may help explain the increased incidence of CKD among
   agricultural workers.

   Keywords: chronic kidney disease of an unknown etiology, climate,
   energy metabolism, exposome, metals, pesticides

Graphical abstract

   [42]graphic file with name ga1.jpg
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     __________________________________________________________________

   See Commentary on [44]Page 1163

   Sugarcane is the most cultivated crop in the world, with nearly 2
   billion tons produced every year. It has a variety of extremely
   valuable applications, ranging from its ubiquitous presence in food and
   increasing role in ethanol production to the use of sugarcane stalk
   pulp (bagasse) in energy production. Therefore, it comes as no surprise
   that production has continued to trend upward over recent
   decades.[45]^1 Sugarcane field work involves a number of distinct job
   tasks, all of which demonstrate some degree of occupational risk and
   increased prevalence of renal injury, especially in cane cutters when
   preventive strategies are uncertain or not fully implemented. Cane
   cutters work an extremely physically demanding job for long hours in
   hot weather with limited rest breaks, shade, and opportunities for
   rehydration; they often work directly with recently burned sugarcane
   without respiratory protection and are exposed to high volumes of
   respirable ash.[46]2, [47]3, [48]4 Heat stress, exertional injury,
   dehydration, and exposure to agricultural toxicants are major
   occupational concerns that are posited as being responsible for the
   growing incidence of CKD among these communities.[49]^5

   In addition to increasing rates of CKD globally and in agricultural
   communities, an epidemic of CKD of unknown etiology (CKDu), also
   referred to as Mesoamerican nephropathy, has been posited to be the
   consequence of performing high exertion work in the face of increasing
   heat plus exposure to nephrotoxicants.[50]6, [51]7, [52]8, [53]9
   Agricultural communities in particular experience disproportionate CKDu
   rates, with hot spots in Latin America, India, and Sri Lanka, among
   other mostly subtropical countries.[54]^10^,[55]^11 Clinically, CKDu is
   defined as CKD that is not a comorbidity of diabetes, hypertension,
   kidney stones, or other known causes of kidney disease. It has a
   gradual onset characterized by a decline in kidney function as measured
   by serum creatinine or serum cystatin C with low or no proteinuria.
   Histopathologically, CKDu is characterized by tubulointerstitial
   nephritis and fibrosis, with glomerulosclerosis appearing in later
   stages of illness.[56]^12^,[57]^13 Unfortunately, the mechanistic
   understanding of CKDu remains extremely limited, impeding the
   development of effective treatments or diagnostic techniques. Given
   that CKDu and the rise in kidney injury are likely linked,
   investigating common exposures in these areas will help elucidate the
   primary risk factors responsible for the rapid degradation of renal
   health. Public health interventions addressing strenuous working
   conditions, heat stress, and dehydration have found some success in
   acute kidney injury mitigation, but such factors are unable to fully
   explain CKDu distribution and prevalence.[58]14, [59]15, [60]16, [61]17
   Thus, there remains a strong possibility that one or more environmental
   toxicants is contributing to the development of this disease via other
   mechanisms of kidney injury.

   Sugarcane agricultural work involves the use of many pesticides,
   fertilizers, and heavy metals, which could result in exposure to these
   nephrotoxic chemicals.[62]^18 Although pesticides and heavy metals have
   a well-documented risk of kidney damage, investigations into their role
   in the development of CKDu are ongoing. Current literature has yet to
   demonstrate a conclusive link, with many studies providing
   contradictory results and meta-analyses not finding any statistically
   significant risks.[63]19, [64]20, [65]21, [66]22, [67]23, [68]24 A more
   recent hypothesis centers on a putative role for silica nanoparticles.
   Sugarcane stalks are comprised primarily of naturally occurring
   amorphous silica, which can be released during routine crop
   burning.[69]^4 Evidence of such exposures have been found in the
   biopsies of some CKDu patients.[70]^25 In addition, these particles
   have been found to be cytotoxic and disruptive of energy metabolism in
   human kidney cells.[71]^26

   Understanding the mechanism of energy metabolism perturbation that
   occurs following exposure to nephrotoxicants could elucidate the
   exposures and stressors that contribute to pathogenesis. Kidney disease
   is known to be associated with inflammatory renal tubular injury,
   altered redox state, fibrosis, and mitochondrial
   dysfunction.[72]^27^,[73]^28 Altered redox state, glycolytic shift, and
   accumulation of fatty acids are hallmarks of CKD that have also been
   demonstrated to occur in vitro following exposure to amorphous silica
   nanoparticles.[74]^26^,[75]^29^,[76]^30 Such processes and pathways
   have many associated metabolites that can be quantified via
   metabolomics to help understand exposures and risk factors correlated
   with higher rates of kidney injury. In addition, elemental and
   pesticide analysis provides a snapshot of the exposome of high-risk
   groups during key periods, clarifying associations between occupational
   and environmental exposures and biological indicators of kidney injury.

   The hypothesis of this study was that the exposome and metabolome
   changes that occur concurrently in sugarcane cutters over the harvest
   season can provide a multifactorial snapshot of early kidney injury,
   which can be used to better understand potential mechanisms and
   biomarkers of CKDu. To this end, paired urine samples were collected
   from cutters at 2 timepoints across the 6-month harvest season.
   Cross-harvest changes to elemental abundance, presence of pesticides,
   and metabolic profiles were investigated to determine trends which may
   correlate with markers of kidney function.

Methods

Study Design

   The data for this analysis were derived from stored urine samples from
   a previous study of male agricultural workers (≥18 years) employed by a
   sugarcane agribusiness in Guatemala. The study was conducted during the
   2017 to 2018 harvest among 202 sugarcane cutters. The harvest season
   lasted 6 months from November through May. For the original study,
   participants were recruited within 2 randomly selected work groups of
   male sugarcane cutters in November 2017. Clinical data and urine and
   blood samples were collected during November 2017 and April 2018 before
   the start of the work shift. These workers live in the Guatemala
   highlands during the rainy season and migrate to the lowlands for
   seasonal work where they are housed in dormitories on company property
   where they predominately consume the same water and prepared meals.
   During the harvest season, agrochemicals were applied, and the
   sugarcane fields were burned to facilitate harvest as a standard
   practice in this region. Additional work setting, worker population,
   work practice, and study method details have been previously
   described.[77]^31^,[78]^32 Participants provided written informed
   consent at the time of recruitment, and institutional review board
   approval for this study was obtained from the Colorado Multiple
   Institutional Review Board of the University of Colorado and Comité de
   Ética Independiente ZUGUEME in Guatemala.

   For this current analysis, untargeted metabolomics and elemental
   analysis were performed on a paired group of urine samples from 20
   workers collected in November and April to determine cross-harvest
   changes, totaling 40 urine samples. To minimize diurnal variation,
   worker urine samples were excluded from selection if collection time
   during the day differed between November and April ([79]Figure 1). Out
   of the original population of 202, 80 workers had morning urine samples
   collected in both November and April. Among these 80 workers, 38 had a
   decline in eGFR from November to April, 19 of whom experienced a
   decline greater than or equal to 9% and were randomly selected from to
   comprise the KFD group. For 42 of the 80 workers, their eGFR maintained
   or increased and had a value greater than 90 at both timepoints, 16 of
   whom maintained kidney function (0%–5% increased eGFR) and were
   randomly selected from to comprise the group with no decline in kidney
   function (non-KFD). The eGFR was calculated from serum creatinine
   values using established methods.[80]^33 Urine creatinine, kidney
   injury marker 1, and electrolyte values were determined via previously
   discussed methods.[81]^32

Figure 1.

   [82]Figure 1
   [83]Open in a new tab

   Study population selection.

Sample Collection and Storage

   Urine samples were morning spot samples collected at approximately the
   same time in November and April before workers began their shift.
   Specific gravity was determined at the time of collection. Samples were
   placed on ice and transported to the on-site clinic within 1 hour after
   collection, where the urine was aliquoted into Fisherbrand sterile
   polypropylene tubes without preservatives before local storage at −20
   ºC and laboratory analysis at Guatemala City. Within 1 week, frozen
   urine aliquots were shipped on dry ice to the University of Colorado
   Anschutz Medical Campus. Upon arrival, they were immediately stored
   at −80 ºC before metabolomic and elemental analysis.

Elemental Analysis

   Urine samples were diluted 1:1 in 70% HNO3 and left to sit at least
   overnight for wet digestion. Samples were then diluted 1:100 in 1.5%
   HNO3 for instrumental analyses. Inductively coupled plasma-mass
   spectrometry analyses were performed on a NexION 2000B single
   quadrupole ICP-MS (Perkin-Elmer, Waltham, MA) equipped with a Meinhard
   nebulizer and a cyclonic spray chamber. Each day, prior to analyses,
   the instrument was tuned with a solution of 1 part per billion Li, Ce,
   In, Pb, and U to optimize sensitivity and robustness. Samples were
   analyzed via total quantitative analysis, scanning from m/z ratios of 7
   to 238 to determine concentration of each individual element in the
   sample. Samples were calibrated to a 1-point external standard point of
   a 10-parts per billion cocktail of 40 metals. Data are presented in
   total parts per billion (μg/l) in urine normalized to creatinine (g/l).

Metabolomics

   All solvents used for extraction and liquid chromatography-mass
   spectrometry analysis were of high-performance liquid chromatography or
   liquid chromatography-mass spectrometry grade, including water from
   Burdick and Jackson (Muskegon, MI) and acetonitrile, methanol, and
   formic acid from Fisher Scientific (Fair Lawn, NJ). Authentic standards
   were from Cayman Chemical (Ann Arbor, MI), Cambridge Isotope
   Laboratories (Tewksbury, MA), CDN Isotopes (Pointe-Claire, Quebec,
   Canada), and Millipore-Sigma (St. Louis, MO).

Sample Preparation

   Urine samples were analyzed neat as previously described with some
   modifications as previously described.[84]^34 Briefly, urine samples
   and a pooled urine quality control (QC) sample were thawed at 4 ^oC,
   and 100 μl aliquots were stored in 1.5 ml microcentrifuge tubes
   overnight at 4°C until sample preparation. Samples and the pooled QC
   samples were centrifuged for 10 minutes at 3000g and 4°C
   (Beckman-Coulter). Then 35 μl of sample supernatants were transferred
   to autosampler vials (Cornerstone Scientific) in a 4 °C cold room. A
   160 μl aliquot of the pooled QC sample, made from 4 μl aliquots of each
   urine sample, was transferred to a fresh 1.5 ml microcentrifuge tube
   (Fisher Scientific) and was spiked with 20 μl each of an in-house
   hydrophilic spike mix and 8-iso PGF2α (Cayman Chemical).[85]^35 The
   spiked pooled QC sample was vortexed well to mix and 40 μl aliquots
   were transferred to autosampler vials. All samples and pooled QC
   aliquots were stored at −80 °C until instrumental analysis.

Liquid Chromatography-Mass Spectrometry

   Urine samples were analyzed by reverse-phase chromatography as
   previously described with the following modifications: the SB-AQ
   analytical column was fitted with an in-line filter frit (Agilent
   Technologies, Santa Clara, CA); mobile phase A was composed of water
   with 0.1% formic acid and 0.1% InfinityLab deactivator additive
   (Agilent Technologies); mobile phase B was composed of acetonitrile
   with 0.1% formic acid and 0.1% InfinityLab deactivator additive; the
   gradient was as follows: 0 to 3 minutes, 1.8% B; 3 to 10 minutes, 1.8%
   to 54% B; 10 to 15 minutes, 54% to 90% B; 15 to 20 minutes, hold at 90%
   B; 20 to 20.1 minutes, 90% to 1.8% B; hold at 1.8% B until 25
   minutes.[86]^34 The Agilent Technologies 6545 Time-of-Flight Mass
   Spectrometer conditions were as previously described.[87]^35

QC

   Pooled QC samples were injected after every 10 samples to monitor
   instrument variability across the run. Detected spiked-in standards had
   coefficient of variations of 6.5% or lower across 7 pooled QC
   injections.

Data Processing and Annotation

   Raw liquid chromatography-mass spectrometry data were extracted using a
   recursive workflow in MassHunter Profinder Version B.10, SP1 (Agilent
   Technologies), similar to that previously described with the following
   modifications: retention time extraction range of 0.5 to 13 minutes
   with a noise peak height filter ≥7500 counts; ion
   species: +H, +Na, +K, +NH4; and charge state maximum of 1. Alignment
   tolerance for RT was 0% + 0.20 minutes with a mass tolerance of 10
   ppm + 2 mDa (millidalton).[88]^34 Data were imported into Mass Profiler
   Professional Version 14.1 (Agilent Technologies) for quality filtering.
   Compounds remaining after filtering were used for targeted feature
   extraction in Profinder. Compounds resulting from targeted data
   extraction were imported into Mass Profiler Professional Version for
   differential analysis. Differential analysis was performed using Mass
   Profiler Professional Version 14.1 (Agilent Technologies), using a
   workflow similar to that previously described.[89]^34 Analysis of
   variance was performed using a P-value of <0.05. A fold change filter
   of 1.5 was applied to analysis of variance results.

Compound Annotation

   Compounds were annotated using MassHunter ID Browser B.08 (Agilent
   technologies) to search in-house and publicly available or commercial
   databases. The in-house database is composed of a set of 683 authentic
   standards; database matches were based on isotope ratios, neutral mass
   (using a 10 ppm window), and retention time. Public and commercial
   databases included HMDB 4.0, Lipid Maps, Metlin and KEGG.[90]36,
   [91]37, [92]38, [93]39 Annotations from public or commercial databases
   were based on accurate mass, with a mass error cutoff of 10 ppm,
   isotope ratios. A match score cutoff of 60 was applied.

Statistical Analysis

   Demographic and biomarker data were compared between time points
   (November and April) and between groups (non-KFD and KFD) using the
   Wilcoxon-Mann Whitney test. Principal component analysis graphs and
   pathway enrichment or topology analysis tables were created using
   MetaboAnalyst 5.0 and the Kyoto Encyclopedia of Genes and Genomes
   (KEGG) database or Small Molecule Pathway Database with a false
   discovery rate corrected P-value (q-value) threshold of 0.05 used to
   determine statistical significance. All other statistical analyses and
   graphics were performed or generated in GraphPad Prism (Version 8.4.3;
   La Jolla, CA) using a paired t-test with Holm-Šídák multiple
   comparisons correction and a P-value threshold of 0.05 to determine
   statistical significance.

Results

Demographics and Laboratory Features

   The demographic features of both groups (non-KFD vs. KFD) reveal that
   the workers with evidence of KFD were, on average, older and weighed
   more at the start of the season ([94]Table 1). Urine creatinine and
   specific gravity indicate more dilute urine at the end of the harvest
   season; however, kidney injury marker 1 levels were still within normal
   ranges at both timepoints. In April, workers with KFD had significantly
   higher levels of urine potassium and chloride compared to non-KFD
   workers.

Table 1.

   Demographic and biomarkers descriptives among sugarcane cutters at the
   start of the harvest, November, and the end of the harvest, April,
   stratified by workers with normal kidney function (non-KFD) and workers
   with evidence of kidney function decline (KFD)
   Characteristics Normal Kidney Function (non-KFD)
   (n = 10)
     __________________________________________________________________

   Kidney Function Decline (KFD)
   (n = 10)
     __________________________________________________________________

   November April November April
   Demographics
   Age, yr, mean (SD) 24 (7) - 29 (3) -
   HbA1c, %, mean (SD) 5.3 (0.1) - 5.4 (0.3)
   Systolic blood pressure, mm Hg, mean (SD) 108 (14.8) 109.6 (7.7) 106
   (8.4) 109.6 (8)
   Diastolic blood pressure, mm Hg, mean (SD) 75 (7.1) 62.9 (4.2)[95]^a 70
   (9.4) 59.5 (9.4)
   Height, m, mean (SD) 1.6 (0.1) - 1.6 (0.1) -
   Weight, kg, mean (SD) 56.5 (6.0) 53.9 (5.5) 62.3 (6.6) 59.2 (8.4)
   Markers of kidney function
   Serum creatinine, mg/dl, mean (SD) 0.79 (0.10) 0.77 (0.10) 0.79 (0.14)
   1.02 (0.18)[96]^a
   eGFR, ml/min per 1.73 m^2, mean (SD) 125.9 (8.1) 127.3 (7.7) 122.9
   (11.6) 103.2 (17.8)[97]^a
   Percent change in eGFR, mean (SD), min–max - 1.1 (0.99), 0–3.00 −21.00
   (13.48), −53.00 to −9.00
   Albumin-to-creatinine ratio, mg/g, mean (SD) 9.3 (6.0) 8.4 (4.5) 6.4
   (5.2) 7.6 (2.3)
   Urine KIM-1, pg/ml (SD) 1014 (817.2) 352.5 (598.3) 649.3 (691.7) 239.7
   (196.9)
   Urine KIM-1, pg/g creatinine 869 (677) 690 (1432) 464 (514) 299 (204)
   Urine biomarkers
   Urine creatinine, mg/dl, mean (SD) 135.8 (67.7) 69.2 (35.3)[98]^a 135.4
   (67.6) 80.5 (40.6)[99]^a
   Urine albumin, mg/l, mean (SD) 14.59 (16.31) 4.67 (1.48)[100]^a 8.19
   (7.13) 5.51 (1.79)
   Urine lactate, mmol/l, mean (SD) 3.09 (1.41) 1.84 (1.06)[101]^a 5.47
   (3.24) 2.02 (0.72)[102]^a
   Urine phosphate, mg/dl, mean (SD) 38.35 (27.75) 22.48 (11.92) 19.44
   (12.66) 29.44 (24.43)
   Urine magnesium, md/dl, mean (SD) 9.54 (5.59) 7.93 (3.21) 7.19 (3.06)
   8.17 (4.24)
   Urine sodium, mmol/l, mean (SD) 60.90 (33.39) 44.6 (28.91) 73.56(31.34)
   63.2 (41.56)
   Urine potassium, mmol/l, mean (SD) 60.96 (26.37) 19.43 (12.82)[103]^a
   47.68 (16.4) 33.38 (10.33)[104]^a
   Urine chloride, mmol/l, mean (SD) 139.0 (52.51) 56.1 (27.79)[105]^a
   130.11 (39.38) 91.2 (38.7)
   Urine specific gravity, mean (SD) 1.019 (0.006) 1.010 (0.005)[106]^a
   1.018 (0.006) 1.013 (0.004)
   Urine pH, mean (SD) 5.85 (0.63) 6.45 (0.83) 6.00 (1.00) 6.05 (0.92)
   [107]Open in a new tab

   eGFR, estimated glomerular filtration rate; KFD, kidney function
   decline; KIM-1, Kidney Injury Molecule-1.
   ^a

   indicates a P-value <0.05 between November and April within groups
   using the Wilcoxon-Mann Whitney test.

Elemental Analysis

   Inductively coupled plasma mass spectrometry analysis revealed
   consistent trends in elemental levels in urine over the harvest season.
   Silicon and phosphorus were found to increase significantly from
   November to April, with silicon levels being higher in every sample
   ([108]Figure 2a) and phosphorus in 85% of samples ([109]Figure 2b).
   Interestingly, the concentration of metals hypothesized to be
   associated with CKDu (i.e., nickel, cadmium, lead, and arsenic)
   remained stable over the harvest season ([110]Figure 2c). Individual
   values for all samples can be found in the supplementary material
   ([111]Supplementary Table S1).

Figure 2.

   [112]Figure 2
   [113]Open in a new tab

   Inductively coupled plasma mass spectrometry (ICPMS) analysis found
   urinary concentrations of silicon (a) and phosphorus (b) significantly
   increased over the harvest season, whereas nephrotoxic metals, nickel,
   cadmium, lead, and arsenic (c) did not change significantly. Each dot
   represents an individual worker sample (n = 20). Dashed lines represent
   upper reference values (in μg/l) from the literature for nickel, from
   the Occupational Safety and Health Administration for cadmium, and from
   the Agency for Toxic Substances and Disease Registry (ATSDR) for
   arsenic.[114]40, [115]41, [116]42 For lead, the dashed line is based on
   the geometric mean of nonsmokers from the ATSDR’s 2020 Toxicological
   Profile for lead.[117]^43 Data were analyzed with a paired t-test with
   Holm-Šídák multiple comparisons correction (∗indicates a P-value <0.05,
   ∗∗ indicates a P-value <0.01). KFD, kidney function decline.

Metabolomic Analysis

   Untargeted metabolomics detected 4799 compounds, 3150 of which were
   present in ≥50% of samples, 3419 of which were present in ≥50% of
   November samples, 2963 of which were present in ≥50% of April samples,
   and 1154 of which were detectable in all samples ([118]Supplementary
   Table S2). Metabolite data were normalized to creatinine, with the
   metabolites demonstrating the greatest change over the harvest season
   expressed in the form of an increased or decreased foldchange heat map
   ([119]Figure 3a). These metabolites consisted primarily of amino acids,
   botanical compounds, and fatty acids. Metabolic changes were consistent
   among subgroups, with minimal variation between KFD and non-KFD
   workers. When visualized in the context of a principal component
   analysis plot ([120]Figure 3b), this becomes even more evident. Despite
   having reduced eGFR, metabolic profiles of KFD workers demonstrated
   high intergroup clustering with non-KFD workers. However, there was
   evidence of cross-harvest changes, with samples from the start of the
   harvest season (November) clearly separating from samples at the end of
   the harvest season (April). To further explore these changes, data were
   uploaded to MetaboAnalyst and underwent pathway enrichment and topology
   analysis within the context of the Small Molecule Pathway Database
   ([121]Table 2) and the KEGG database ([122]Supplementary Table S3).
   There were several significantly impacted pathways, including altered
   amino acid metabolism, fatty acid oxidation, glutathione homeostasis,
   and various energetic pathways (i.e., pyruvate and carnitine
   metabolism). In addition, there were signs of perturbation to
   nicotinamide adenine dinucleotide (NAD+) biosynthesis in the form of
   altered tryptophan, nicotinate, and nicotinamide metabolism. Specific
   compound hits from pathway analysis ([123]Table 3) followed similar
   trends across the harvest season, with comparable fold change values
   between KFD and non-KFD workers ([124]Supplementary Figure S1).

Figure 3.

   [125]Figure 3
   [126]Open in a new tab

   Heat map of metabolite fold change from November to April (a) reveals
   highest up and down regulated metabolites are primarily those
   associated with fatty acid and amino acid metabolism. Principle
   component analysis (PCA) plots (b) demonstrate high clustering based on
   time point among all workers (top figure) and among both kidney
   function decline (KFD) and non-KFD agricultural workers (bottom
   figure). Original compound values were log2 transformed, normalized to
   creatinine, and analyzed via metaboanalyst (n = 20). KFD, kidney
   function decline.

Table 2.

   Significantly impacted pathways based on the Small Molecule Pathway
   Database with P-values from pathway enrichment analysis and pathway
   impact values from pathway topology analysis
   Pathway Total compounds Hits November vs. April (All) November
   (Non-KFD) vs. April (Non-KFD) November (KFD) vs. April (KFD) April
   (Non-KFD) vs. April (KFD) Impact
   Betaine Metabolism 18 3 (Betaine aldehyde; Betaine;Homocysteine)
   5.0252E-07 0.008983 0.000020551 0.11756 0.34862
   Carnitine Synthesis 16 1 (3-Hydroxy-N6,N6,N6-trimethyl-L-lysine)
   0.00084994 0.038378 0.0047409 0.88928 0.32941
   Histidine Metabolism 35 1 (L-Histidine) 0.014709 0.010182 0.18375
   0.75647 0.23416
   Methionine Metabolism 39 3 (Homocysteine; Sarcosine; Betaine)
   3.8911E-07 0.0085505 0.000020551 0.75074 0.14308
   Tryptophan Metabolism 55 2 (3-Hydroxyanthranilic acid;
   Tetrahydrobiopterin) 0.000058313 0.01131 0.004632 0.32579 0.071296
   Glycine and Serine Metabolism 50 4 (Pyruvaldehyde; Homocysteine;
   Betaine; Sarcosine) 1.0558E-08 0.00080861 0.000020551 0.011282 0.067324
   Beta-Alanine Metabolism 26 2 (L-Histidine; Ureidopropionic acid)
   0.0027437 0.010182 0.078916 0.006329 0.066116
   Androstenedione Metabolism 23 2 (Etiocholanolone glucuronide;
   Etiocholanolone) 0.00036168 0.0085505 0.072899 0.3314 0.053333
   Pyrimidine Metabolism 54 2 (Ureidopropionic acid; Ureidoisobutyric
   acid) 0.00015386 0.010182 0.013997 0.006329 0.029225
   Pterine Biosynthesis 18 1 (Tetrahydrobiopterin) 0.0031783 0.029289
   0.073112 0.3751 0.012869
   Nicotinate and Nicotinamide Metabolism 32 1 (Nicotinamide riboside)
   0.0010067 0.018867 0.039495 0.34175 0.010436
   Tyrosine Metabolism 55 3 (Ascorbic acid; Tetrahydrobiopterin;
   5,6-Dihydroxyindole) 0.000058119 0.000066355 0.06462 0.32579 0.0014347
   Ammonia Recycling 25 1 (L-Histidine) 0.014709 0.010182 0.18375 0.32658
   0
   Catecholamine Biosynthesis 14 1 (Ascorbic acid) 0.00056843 0.0085505
   0.058653 0.13075 0
   Homocysteine Degradation 7 1 (Homocysteine) 1.4172E-06 0.010182
   0.000020551 0.74332 0
   Pyruvaldehyde Degradation 7 1 (Pyruvaldehyde) 3.2267E-09 0.000054311
   0.00012806 0.8889 0
   Pyruvate Metabolism 37 1 (Pyruvaldehyde) 3.2267E-09 0.000054311
   0.00012806 0.89075 0
   Vitamin B6 Metabolism 15 1 (4-Pyridoxic acid) 0.00064053 0.010587
   0.031123 0.077747 0
   [127]Open in a new tab

   KFD, kidney function decline.

   Data is ordered by descending impact factor (q value <0.05 is indicated
   by a bolded value). Original compound values were log2 transformed and
   analyzed via metaboanalyst (N = 20).

Table 3.

   Compound hits from pathway analysis with fold change from November to
   April and across groups with or without KFD
   Compounds November vs. April (All) November (Non-KFD) vs. April
   (Non-KFD) November (KFD) vs. April (KFD) April (Non-KFD) vs. April
   (KFD)
   Betaine aldehyde 1.0394004 1.1351618 0.943639 −0.7584812
   3-Hydroxy-N6, N6,N6-trimethyl-L-lysine 1.1236717 1.1721541 1.0751893
   −0.4505893
   L-Histidine 0.7307202 0.9475248 0.5139156 −1.18501835
   Homocysteine 3.42772305 2.8106313 4.0448148 −0.0611859
   Sarcosine 1.359087625 1.44399085 1.2741844 −1.0571272
   Betaine 0.6151725 0.55932625 0.67101875 −0.07658775
   3-Hydroxyanthranilic acid 1.45654495 0.5908845 2.3222054 −0.1527283
   Tetrahydrobiopterin 0.956942125 1.01491395 0.8989703 −0.1508771
   Pyruvaldehyde 2.0163203 2.2148971 1.8177435 −0.3794771
   Ureidopropionic acid 0.670869 0.73045375 0.61128425 −0.19337995
   Etiocholanolone glucuronide −0.19546855 −0.32636545 −0.06457165
   −0.0285683
   Etiocholanolone −0.993823325 −1.3519187 −0.63572795 0.26880215
   Ureidoisobutyric acid 1.03568835 1.16121055 0.91016615 −0.0829997
   Nicotinamide riboside 1.030189025 1.1395568 0.92082125 −0.40516915
   Ascorbic acid 1.3980836 1.5413648 1.2548024 0.0890468
   5,6−Dihydroxyindole −0.79335603 −1.23406921 −0.35264285 0.93608066
   4-Pyridoxic acid 2.276566175 3.1774513 1.37568105 −0.4154801
   [128]Open in a new tab

   KFD, kidney function decline.

   Original compound values were log2 transformed, normalized to
   creatinine, and analyzed via metaboanalyst (n = 20).

Metabolite Abundance Trends

   Fatty acid accumulation is a hallmark of CKD and is indicative of
   impaired beta oxidation, reduced mitochondrial function, and perturbed
   energy metabolism. Metabolites associated with such pathways were
   quantified and analyzed via paired t-tests between November and April
   timepoints ([129]Figure 4a). N6,N6,N6-trimethyl-L-lysine,
   valerylcarnitine, deoxycarnitine, dodecanedioylcarnitine,
   L-hexanoylcarnitine, and octanoyl-L-carnitine were found to increase
   significantly in abundance. Propionyl-L-carnitine abundance was found
   to decrease. Next, we similarly investigated amino acid metabolites
   known to correlate with compromised renal function, kidney injury, and
   disease progression ([130]Figure 4b). Histidine, L-isoleucine,
   methionine, proline, valine, sarcosine, pyrimidine, and homocysteine
   were all found to be higher in abundance at the end of the harvest
   season.

Figure 4.

   [131]Figure 4
   [132]Open in a new tab

   Trends in fatty acid (a) and amino acid (b) metabolites over the
   harvest season among all workers were consistent with known signs of
   impaired renal function and kidney disease. Data is presented as log2
   signal intensity normalized to creatinine and each dot represents an
   individual worker sample (n = 20). Data were analyzed with a paired
   t-test with Holm-Šídák multiple comparisons correction (∗ indicates a
   P-value <0.05, ∗∗ indicates a P-value <0.01, ∗∗∗ and ∗∗∗∗ indicate a
   P-value <0.001 and <0.0001, respectively).

Pesticide Metabolomics

   Metabolite compounds demonstrating ≥1.5-fold change over the harvest
   season ([133]Supplementary Table S4) were searched against a pesticide
   database and expressed as a heat map ([134]Figure 5a). Although the
   majority of detected pesticide metabolites were not found to increase
   from November to April, carbofuran-3-keto, metolachlor, diquat, and
   paraquat were found to increase significantly over the harvest season
   ([135]Figure 5b).

Figure 5.

   [136]Figure 5
   [137]Open in a new tab

   Heat map of pesticide metabolite trends from November to April. Overall
   pesticide metabolite trends remained consistent across groups (a).
   Workers with normal kidney function (non-KFD) demonstrated greater
   apparent decreases in some metabolites over the harvest season;
   however, metabolite abundance of certain potentially nephrotoxic
   pesticides increased across the season in both groups (b). Original
   compounds were referenced to a pesticide database, values were log2
   transformed, and normalized to creatinine. Data is presented as fold
   change over the harvest season (n = 20). Potentially nephrotoxic
   pesticide metabolites were analyzed with a paired t-test with
   Holm-Šídák multiple comparisons correction (∗ indicates a
   P-value <0.05, ∗∗ indicates a P-value <0.01). KFD, kidney function
   decline.

Discussion

   This study demonstrates the value of a combined exposome and metabolome
   analysis, providing insights into agricultural workers’ key
   occupational risk factors and potential mechanisms of CKDu pathogenesis
   in agricultural field workers exposed to complex climatologic and
   environmental exposure conditions.

   Exposomic analysis of urine samples revealed elevated levels of silicon
   over the harvest season, indicating a consistent occupational exposure
   among sugarcane cutters, with potential renal health risks. Nephrotoxic
   metals remained low, suggesting minimal exposure. Several pesticides,
   including some which have been implicated as potentially nephrotoxic,
   were found to increase in abundance from November to April. This
   demonstrates evidence of pesticide uptake and may help explain the
   metabolome changes which were found to occur over the harvest season,
   including altered energy metabolism and trends in metabolite abundance
   consistent with CKD biomarkers. Understanding these exposures and the
   key risk factors at play is critical as new epidemics of kidney
   disease, including CKDu, continue to increase around the world.[138]^44

   Exposure to heavy metals has long been known to cause renal damage and
   is suspected of playing a key role in recent epidemics of kidney
   injury, due to use of agrochemicals known to contain nephrotoxic metals
   and some evidence of elevated heavy metal levels in agricultural
   communities’ water sources.[139]^21^,[140]45, [141]46, [142]47,
   [143]48, [144]49 Indeed, arsenic, cadmium, and lead are pervasive
   toxicants with established mechanisms for inducing both acute kidney
   injury and CKD, resulting in ongoing investigations into potential
   contributions to the epidemics of CKDu occurring
   worldwide.[145]^50^,[146]^51 Although some association of heavy metals
   have been made with increased risk of CKDu, such findings have been
   inconsistent.[147]^23^,[148]^24 Thus, it is unsurprising that elemental
   analysis found that urinary levels of heavy metals were relatively low
   and did not increase significantly over the harvest.[149]40, [150]41,
   [151]42, [152]43 It is important to emphasize that the agricultural
   company where these workers were employed had implemented a program to
   reduce worker exposure to heavy metal contaminated water through
   routine QC testing for metals in their supplied drinking water. We
   would caution against generalizing from the observation of low heavy
   metal exposure because the exposure control efforts of this company may
   not be representative of the industry at large and urine samples are
   less accurate than blood tests for determining chronic exposure to
   certain metals.

   Unlike heavy metals, urinary abundance of silicon increased over the
   harvest season. This confirms that silicon is a concerning occupational
   exposure and lends additional support to evidence of cane worker
   exposure and absorption of silica nanoparticles via ash inhalation from
   sugarcane burning and/or groundwater consumption.[153]^52 Such
   amorphous silica carries known risks and can be resistant to clearance,
   potentially leading to accumulation after each harvest.[154]53,
   [155]54, [156]55, [157]56, [158]57, [159]58 This accumulation in the
   kidney tissue, as has been demonstrated in worker biopsies, may limit
   urinary excretion and could explain why abundance did not differ
   significantly between KFD and non-KFD groups.[160]^59 Elevated
   phosphorus is also notable as a known indicator of renal damage
   resulting in altered kidney function. Renal proximal tubular cells are
   responsible for maintaining phosphate homeostasis through reabsorption;
   exposure to nephrotoxic metals, pesticides, and/or silica could damage
   these cells and result in increased urinary phosphate
   excretion.[161]^60^,[162]^61 However, such damage would generally be
   expected to be accompanied by increased kidney injury marker 1 levels,
   rather than the slight cross-harvest decrease observed. High phosphate
   has been found to be associated with reactive oxygen species
   generation, mitochondrial dysfunction, cell death, and progression of
   CKD.[163]62, [164]63, [165]64, [166]65, [167]66 Interestingly, similar
   hallmarks of kidney disease have been found in vitro following silica
   nanoparticle exposure and in animal models following sugarcane ash
   exposure.[168]^26^,[169]^57^,[170]^58 As with silicon, phosphorus
   abundance was comparable between KFD and non-KFD workers despite
   increasing over the harvest season. One reason for this could be that
   reduced glomerular filtration rate can limit renal clearance, causing
   levels to appear lower than expected, particularly when normalizing
   elemental expression to creatinine.

   Pesticides are another occupational and community exposure of concern,
   due in part to their abundant use in agricultural communities and
   tendency to contain nephrotoxic components. Previous epidemiologic
   investigations into the link between pesticide exposure and CKDu have
   produced varying results, with several finding positive
   associations.[171]^20^,[172]^21^,[173]67, [174]68, [175]69 Carbofuran,
   metolachlor, paraquat, and diquat are 4 of such compounds which are
   known to cause acute kidney injury and have been previously implicated
   in CKD.[176]^67^,[177]69, [178]70, [179]71, [180]72, [181]73, [182]74
   In particular, a recent study found evidence that paraquat might have a
   participatory role in CKDu from Central America.[183]^75 These
   pesticides and associated metabolites were found to increase in
   abundance over the harvest season, across both KFD and non-KFD workers.
   Even if such exposures are not solely responsible for elevated rates of
   kidney disease seen among this population, they are yet another
   nephrological stressor that could be contributing to pathogenesis. CKDu
   etiology is likely multifactorial. We speculate that even low levels of
   pesticide exposure could be hazardous if occurring alongside chronic
   exposure to silica, heavy metals, heat stress, and dehydration.
   Characterizing the metabolomic state of individuals who experience
   multiple exposures is essential to determine specific mechanisms and
   pathways involved in pathogenesis.

   Alteration in energy metabolism is a hallmark of CKD, which has been
   found to occur in populations at high risk for CKDu.[184]^28 The kidney
   is an extremely energy-intensive organ, requiring large amounts of
   functional mitochondria to remain healthy.[185]^76 It is therefore
   highly sensitive to any changes in energy production, with disease
   states demonstrating pronounced shifts in energy metabolism.
   Nicotinamide metabolism was altered significantly over the harvest
   season, along with carnitine synthesis, pyruvate metabolism, and
   expression of several fatty acid metabolites. NAD+ is a crucial
   coenzyme for a variety of cellular processes, including DNA repair,
   cellular senescence, and energy metabolism.[186]^77 Perturbed energy
   metabolism associated with NAD+ is known to occur in states of kidney
   stress, with depletion leading to ischemia and renal tubular
   injury.[187]78, [188]79, [189]80 Increased abundance of free fatty
   acids is indicative of inhibited beta oxidation of fatty acids, a key
   mechanism of ATP generation in the kidney.[190]^29^,[191]81, [192]82,
   [193]83, [194]84, [195]85 This trend is not only linked with CKD, but
   specific fatty acid metabolites were found to follow the same pattern
   as occurred in vitro following exposure to silica nanoparticles derived
   from sugarcane ash.[196]^26 Furthermore, the only fatty acid associated
   metabolite that was found to undergo a significant decrease both over
   the harvest season and in vitro was propionyl-l-carnitine, a metabolite
   which has known nephroprotective effects.[197]^86^,[198]^87 Beyond
   nicotinamide and lipid metabolism perturbation, a variety of amino acid
   metabolites (i.e., histidine, methionine, or valine) were found to
   demonstrate signs consistent with impaired renal function and kidney
   disease.[199]88, [200]89, [201]90, [202]91, [203]92, [204]93, [205]94,
   [206]95 Phenylacetic acid and acetaminophen glucuronide were other
   metabolites of concern found to increase in abundance over the harvest
   season, the former of which has been found to be elevated in CKD
   patient and both of which are capable of inducing inflammation and
   oxidative stress.[207]96, [208]97, [209]98, [210]99, [211]100 These
   changes are consistent with inhibition of mitochondrial respiration,
   perturbed lipid metabolism, and glycolytic shifts that occur during
   ischemic acute kidney injury. If injury is so severe that cells are
   unable to recover from the stress and energy production remains
   insufficient, this can be followed by proximal tubule atrophy,
   fibrosis, and a transition to CKD.[212]^101 It is this pathology that
   is seen both in CKDu patient biopsies as well as in vivo models of
   silica exposure.

   There are several limitations which must be acknowledged and considered
   to contextualize these findings. The sample size and timepoints of this
   study are key considerations. Although many elemental and metabolic
   trends were consistent throughout groups and over the harvest season,
   it is unclear whether such findings would remain consistent over
   multiple harvests or would be observed in other CKD-prone populations
   (i.e., Sri Lankan/Indian sugarcane harvesters). In addition, it is
   unknown if any of the workers included in this study later developed
   signs of renal dysfunction, it is possible that observed trends might
   be more closely associated with long-term kidney health issues rather
   than the “snapshot” eGFR values used to assign groups in the current
   study. The use of creatinine to normalize for sample concentration also
   may obscure some group variation. Such factors could explain why
   elemental abundance and metabolic trends were consistent over the
   harvest but varied minimally across KFD/non-KFD groups. Another
   limitation is the use of untargeted liquid chromatography-mass
   spectrometry, which is valuable for initial exploration and trend
   identification, and should ideally be followed-up with more targeted
   approaches to confirm suspected metabolic and pathway changes. The use
   of urine as a biomatrix also limits heavy metal analysis, particularly
   given the discussed company water interventions. To properly evaluate
   chronic metal exposure, a blood test in addition to urine analysis
   would be ideal. Lastly, it is important to recognize that the
   experimental approach of this study is unable to determine
   directionality of associations. Changes to renal function, such as
   altered glomerular filtration rate or compromised proximal convoluted
   tubule uptake, could greatly impact elemental and metabolic expression.
   Therefore, though the findings of this study provide a promising start,
   future research is needed to further elucidate the role of perturbed
   lipid or amino acid msilica,ism, silica, and pesticides in CKD
   pathogenesis.

Disclosure

   All the authors declared no competing interests.

Acknowledgments