Laboratory correlates of SARS-CoV-2 seropositivity in a nationwide sample of patients on dialysis in the U.S.

Patients on dialysis are at high risk for death due to COVID-19, yet a significant proportion do survive as evidenced by presence of SARS-CoV-2 antibodies in 8% of patients in the U.S. in July 2020. It is unclear whether patients with seropositivity represent the subgroup with robust health status, who would be more likely to mount a durable antibody response. Using data from a July 2020 sample of 28,503 patients receiving dialysis, we evaluated the cross-sectional association of SARS-CoV-2 seropositivity with laboratory surrogates of patient health. In separate logistic regression models, we assessed the association of SARS-CoV-2 seropositivity with seven laboratory-based covariates (albumin, creatinine, hemoglobin, sodium, potassium, phosphate, and parathyroid hormone), across the entire range of the laboratory and in comparison to a referent value. Models accounted for age, sex, region, race and ethnicity, and county-level COVID-19 deaths per 100,000. Odds of seropositivity for albumin 3 and 3.5 g/dL were 2.1 (95% CI 1.9-2.3) and 1.3 (1.2-1.4) respectively, compared with 4 g/dL. Odds of seropositivity for serum creatinine 5 and 8 mg/dL were 1.8 (1.6-2.0) and 1.3 (1.2-1.4) respectively, compared with 12.5 mg/dL. Lower values of hemoglobin, sodium, potassium, phosphate, and parathyroid hormone were associated with higher odds of seropositivity. Laboratory values associated with poorer health status and higher risk for mortality were also associated with higher likelihood of SARS-CoV-2 antibodies in patients receiving dialysis.

Introduction

Patients receiving dialysis are at high risk for COVID-19 related hospitalizations [1] and if hospitalized, high risk of death. Studies from Europe [2-5], Latin America [6], and the U.S. [7, 8] report that nearly one-third may die if hospitalized, and up to half may die if they require intensive care [8]. In addition to presenting features of illness—e.g., fever or cough, lymphopenia, or elevated serum ferritin—underlying patient health status are risk factors for death [7, 9].

However, hospitalized patients represent a small fraction of patients with SARS-CoV-2 infection, since growing national and international data indicate that only approximately 10% of patients with presence of SARS-CoV-2 antibody are diagnosed as cases [10, 11]. In our recent analysis of remainder sera [12], 8% of patients receiving dialysis had evidence of SARS-CoV-2 S1 spike receptor binding domain (S1RBD) total antibody in July 2020, implying that a sizeable number were infected and survived SARS-CoV-2 exposure. Clarke et al. reported that 40% of patients on dialysis were asymptomatic and/or undiagnosed despite showing evidence for SARS-CoV-2 nucleocapsid antibody [13].

Evaluating correlates of SARS-CoV-2 seropositivity, rather than of diagnosed or hospitalized cases, therefore captures a larger range of affected patients. While poorer health status may be associated with higher risk of death among patients on dialysis hospitalized with COVID-19, it is not known whether poorer health status is also a risk factor for SARS-CoV-2 seropositivity. One major consideration is that SARS-CoV-2 seropositivity implies ability to mount an antibody response to infection, which may be impaired among older and frail patients receiving dialysis [14-16].

Several routinely collected laboratory studies are valid surrogates of health status in patients receiving dialysis [17-20]. In the current study, we investigated the association of routinely collected laboratory tests with the presence of SARS-CoV-2 total antibody. Recognizing the interconnections among overall health, nutritional status, inflammation, and the immune response, we hypothesized that after accounting for community burden of COVID-19, patients with laboratory surrogates of more robust health status (i.e., higher serum concentrations of albumin, creatinine, and hemoglobin) would more likely have survived a SARS-CoV-2 infection and mounted an antibody response than patients with less robust health status. We also explored the odds of seropositivity associated with other routinely measured laboratory tests.

Materials and methods

In partnership with Ascend Clinical Laboratory, a central laboratory that receives laboratory samples from approximately 65,000 patients receiving dialysis throughout the U.S., we tested a random sample of 28,503 patients for SARS-CoV-2 total antibody in July. The Stanford University Institutional Review Board 61 (Registration 4947) approved this work under study protocol #56901. The data were fully anonymized before Stanford University researchers analyzed them; all sample analyses were performed as part of routine clinical care or using plasma that would have otherwise been discarded (for the SARS-CoV-2 antibody testing). The Stanford University IRB waived requirement for informed consent.

Sampling procedures, covariates and outcome

Details of our sampling methodology and covariate definitions are published elsewhere [12]. Briefly, in order to attain representativeness of patients on dialysis in the U.S., we used implicit stratification for sampling accounting for age, sex, and U.S. census region (using patient zip code to assign region). The age and sex distribution of patients included in our sample matched the United States Renal Data System reported distribution as of January 1, 2017 [21].

Among sampled patients, we extracted electronic health record data to ascertain demographics, residence zipcode, and comorbidities. Patients’ electronic health data on demographics and comorbidities as available to Ascend Clinical Laboratory were accessed for the month of the seroprevalence analysis (July 2020). Patients’ laboratory data—performed as part of routine clinical care—were accessed dating back 6 months prior to seroprevalence testing (January 2020). The source of data were Ascend Clinical Laboratory and electronic medical record data; all data were anonymized prior to analysis.

We assigned each patient to one of four regions using U.S. Census classification of the Northeast, South, Midwest, and West. We also assigned a community burden of COVID19 via linkage to county-level data from the Center for Systems Science and Engineering at Johns Hopkins University [22]; we selected the metric of county cumulative deaths per 100,000 as of June 30, 2020 on the basis of our prior analyses [12]. We used the Siemens Healthineers S1RBD total immunoglobulin assay to define seropositivity [23].

Laboratory studies

We extracted available data from July for the following routinely collected laboratory studies: serum albumin, creatinine, hemoglobin, sodium, potassium, phosphate, and parathyroid hormone (PTH), all measured at the central laboratory using standardized assays. Albumin was measured using the Bromcresol Green (BCG) method. Ninety five percent of sampled patients had analyzed laboratories drawn on the same date as the SARS-CoV-2 antibody testing. If more than one value were available for a patient during the month, we used the value nearest in date to his or her SARS-CoV-2 antibody testing. In order to evaluate the possibility of reverse causation, that is, if an association with poorer health status and seropositivity emerged, it was due to antecedent infection with SARS-CoV-2 leading to a decline in health status, we selected serum albumin as the laboratory surrogate most likely to be affected by infection and the inflammatory response. Furthermore unlike other potential markers of health status (e.g., phosphate or parathyroid hormone) [24], serum albumin has a strong and consistently observed inverse association with mortality among patients on dialysis [17, 25, 26]. We thus extracted data for serum albumin starting prior to the emergence of SARS-CoV-2 (January 2020).

Statistical analyses

We provide demographic data and laboratory concentrations using proportions, mean ± standard deviation (SD) or median with 25th and 75th percentile, as applicable, stratified by region. In separate logistic regression models accounting for age, sex, region, ZCTA, and community death rate, we evaluated the association between the selected laboratory covariate and seropositivity for SARS-CoV-2. We modeled all laboratory covariates as continuous variables either imposing a linear relationship with the logit of seropositivity for SARS-CoV-2 or assuming a non-linear relationship and using restricted cubic splines [27]. The best model fit was determined from an analysis of binned residuals [28]. We plotted odds ratios of seropositivity across the span of the laboratory range for a meaningful decrease in the laboratory value as well as in comparison to a fixed reference value. We set the reference value on the basis of the seminal analysis by Lowrie and Lew [17] describing the association of mortality with commonly measured laboratory concentrations in patients on dialysis. For two laboratory concentrations not assessed in this original work, we considered Xia et al. [20] to assign the hemoglobin reference at 10 g/dL and Tentori et al. [29] to assign the parathyroid hormone reference at 300 pg/mL. For serum albumin, we report monthly median (25th, 75th percentile) concentrations from January to July by SARS-CoV-2 seropositivity status in July; we used the using Wilcoxon rank-sum test to compare the distributions. We used multiple imputation by age group to account for missing covariate data. Data analyses were performed using SAS and Stata.

Results

Of the 28,503 patients in the sample two did not have data on residence zip code and were thus dropped from further analysis; 8% (n = 2292) were seropositive for SARS-CoV-2 total antibody in July 2020. Approximately half of our sampled patients were aged 65 years or above, and the majority (57%) were men (Table 1). There were fewer octogenarians in the South, and lower mean serum creatinine concentrations were observed in the Midwest. There was substantially higher missingness of PTH concentrations in the West.

Table 1

Sampled patient characteristics by region.

Patient characteristics	Selected Sample	Northeast	South	Midwest	West
	N = 28501	N = 4536	N = 10937	N = 3763	N = 9265
Age
18–44	3,303 (11.6)	439 (9.7)	1337 (12.2)	399 (10.6)	1128 (12.2)
45–64	11539 (40.5)	1683 (37.1)	4703 (43.0)	1417 (37.7)	3736 (40.3)
65–79	10220 (35.9)	1749 (38.6)	3831 (35.0)	1449 (38.5)	3191 (34.4)
≥80	3439 (12.1)	665 (14.7)	1066 (9.8)	498 (13.2)	1210 (13.1)
Sex
M	16348 (57.4)	2626 (57.9)	6194 (56.6)	2101 (55.8)	5427 (58.6)
F	12153 (42.6)	1910 (42.1)	4743 (43.4)	1662 (44.2)	3838 (41.4)
Home therapy^	1788 (6.3)	208 (5.5)	105 (2.3)	737 (6.7)	738 (8.0)
Race and Ethnicity
Hispanic	3187 (11.2)	119 (2.6)	1641 (15.0)	83 (2.2)	1344 (14.5)
Non-Hispanic white	6532 (22.9)	890 (19.6)	2868 (26.2)	1100 (29.2)	1674 (18.1)
Non-Hispanic Black	4893 (17.2)	880 (19.4)	2963 (27.1)	524 (13.9)	526 (5.7)
Non-Hispanic Other	2479 (8.7)	280 (6.2)	253 (2.3)	78 (2.1)	1868 (20.2)
Unknown	11410 (40.0)	2367 (52.2)	3212 (29.4)	1978 (52.6)	3853 (41.6)
ZCTA Majority Race and Ethnicity+
Non-Hispanic white	8733 (30.6)	1435 (31.6)	3329 (30.4)	2271 (60.4)	1698 (18.3)
Non-Hispanic Black	2585 (9.1)	564 (12.4)	1276 (11.7)	670 (17.8)	75 (0.8)
Hispanic	4568 (16.0)	383 (8.4)	2263 (20.7)	114 (3.0)	1808 (19.5)
Hispanic and Black	2878 (10.1)	734 (16.2)	1488 (13.6)	139 (3.7)	517 (5.6)
Other	9737 (34.2)	1420 (31.3)	2581 (23.6)	569 (15.1)	5167 (55.8)
Laboratory concentrations
Hemoglobin, g/dL	10.7 (1.4)	10.5 (1.4)	10.7 (1.5)	10.7 (1.5)	10.8 (1.4)
Missing	33 (0.1)	4 (0.1)	12 (0.1)	3 (0.1)	14 (0.2)
Potassium, mEq/L	4.7 (0.7)	4.7 (0.7)	4.7 (0.7)	4.6 (0.7)	4.8 (0.7)
Missing	56 (0.2)	8 (0.2)	31 (0.3)	1 (0.03)	16 (0.2)
Phosphorus, mg/dL	5.60 (1.8)	5.48 (1.7)	5.65 (1.8)	5.49 (1.7)	5.6 (1.8)
Missing	46 (0.2)	10 (0.2)	0.17 (0.2)	1 (0.03)	18 (0.2)
Creatinine, mg/dL	8.61 (3.3)	8.74 (3.4)	8.62 (3.4)	8.03 (3.3)	8.8 (3.2)
Missing	59 (0.2)	10 (0.2)	30 (0.3)	3 (0.1)	16 (0.2)
Sodium, mEq/L	138.2 (3.8)	138.2 (3.8) (3.83)	138.6 (3.7) (3.70)	138.4 (3.9) (3.92)	137.5 (3.8)
Missing	126 (0.4)	12 (0.3)	88 (0.8)	9 (0.2)	17 (0.2)
Albumin, g/dL	3.7 (0.4)	3.7 (0.4)	3.7 (0.4)	3.7 (0.4)	3.8 (0.4)
Missing	140 (0.5)	7 (0.2)	17 (0.2)	102 (2.7)	14 (0.2)
PTH*, pg/mL	350 (209,554)	356(209,575)	351 (211,550)	322 (194,509)	361 (217,579)
Missing	4886 (17.1)	290 (6.4)	1248 (11.4)	215 (5.7)	3133 (33.8)

Table reports count (percent) or mean (SD) except as noted.

^a participant was defined as on a home modality if he/she had measures of weekly total kt/v *Median (25th, 75th percentile).

ZCTA Majority defined as population in ZCTA ≥ 60% Hispanic, Non-Hispanic Black, or Non Hispanic White; if in remainder ZCTAs Hispanic and Black population exceeded ≥60%, ZCTA defined as ‘Hispanic and Black’, else as ‘Other’. Abbreviations: ZCTA-zip code tabulation area, PTH-parathyroid hormone.

Relations with laboratory values

In adjusted models comparing to a reference value, odds of seropositivity were higher among patients with serum albumin below 4 g/dL, serum creatinine below 12.5 mg/dL, and hemoglobin below 10 g/dL (Fig 1 Panels A-C). Odds of seropositivity for serum albumin 3 and 3.5 g/dL were 2.1 (95% CI 1.9–2.3) and 1.3 (1.2–1.4) respectively, compared with 4 g/dL. Odds of seropositivity for serum creatinine 5 and 8 mg/dL were 1.8 (1.6–2.0) and 1.3 (1.2–1.4) respectively, compared with 12.5 mg/dL.

Fig 1

Relations among three laboratory surrogates of health status and SARS-CoV-2 seropositivity.

Panels A, B, and C show odds of seropositivity compared with reference values of 4 g/dL, 12.5 g/dL, 10 g/dL for albumin, creatinine, and hemoglobin, respectively. For example, in panel A odds of seropositivity for albumin 3 and 3.5 g/dL were 2.1 (95% CI 1.9–2.3) and 1.3 (1.2–1.4) respectively, compared with 4 g/dL. Panels D, E, and F show odds of seropositivity relative to 0.5 g/dL, 1 mg/dL, and 1 g/dL decrease in serum albumin, creatinine, and hemoglobin, respectively. For example, in panel E, when creatinine equals 5 mg/dL, the plot shows the odds ratio (OR) comparing a decrease in creatinine from 5 mg/dL to 4 mg/dL. Models account for differences in SARS-CoV-2 seroprevalence by age, sex, region, county level deaths per 100,000 from SARS-CoV-2, and zip code tabulation area racial or ethnic mix.

In adjusted continuous models, serum albumin and creatinine correlated inversely with odds of SARS-CoV-2 seropositivity (Fig 1 Panel D,E). For example, odds of seropositivity were 58% higher in a patient with serum albumin 3.0 g/dL compared to a patient with serum albumin 3.5 g/dL. Odds of seropositivity were 12% higher in patients with serum creatinine 6 mg/dL compared to patients with serum creatinine 7 mg/dL. The relation plateaued above a threshold, such that relative differences in likelihood of seropositivity comparing serum albumin 4.0 g/dL versus 4.5 g/dL and serum creatinine 11 g/dL versus 12 g/dL were minimal. For hemoglobin, there was a suggestion of a bimodal relationship such that odds of seropositivity were highest with hemoglobin 9–10 g/dL (Fig 1 Panel F).

Serum sodium and potassium concentrations, serum phosphate, and PTH correlated inversely with odds of seropositivity (S1 and S2 Figs). Compared to a referent potassium at 5 meq/L, however, while patients with lower serum potassium had higher odds of seropositivity, these odds were also higher for seropositivity at potassium concentrations above 5.0 meq/L (S1 Fig Panel 1A).

We also evaluated whether age modified the association between albumin and creatinine, and seropositivity. We found that in general older patients had higher risk for SARS-CoV-2 seropositivity than younger patients below the referent values (S3 and S4 Figs).

Distribution of antecedent serum albumin concentrations

In comparing the distribution of monthly serum albumin concentrations stratified by SARS-CoV-2 seropositive status in July, we found that medians were identical (3.8 g/dL) in seropositive and seronegative groups prior to the emergence of the pandemic in the U.S. (Fig 2). In the seropositive group, albumin concentrations dropped during the first SARS-CoV-2 wave (March-April-May). The nadir occurred in May, with median albumin concentration 3.6g/dL (25th, 75th percentile: 3.3, 3.9 g/dL) versus 3.8g/dL (25th, 75th percentile: 3.5,4.0 g/dL) among seropositive versus seronegative patients, respectively (p-value < 0.0001). Consistently, in January 2020, similar proportions of patients with and without seropositivity had albumin < 3.5 g/dL (23 versus 22%, p = 0.21). By May 2020, 39% of patients with versus 22% of patients without SARS-CoV-2 seropositivity had serum albumin < 3.5 g/dL (p < 0.0001).

Fig 2

Serum albumin concentrations among patients with and without SARS-CoV-2 antibodies in July 2020.

Among patients who did not have SARS-CoV-2 antibodies in July, the serum albumin concentrations remained stable between January and July 2020 (median 3.8 [25th, 75th percentile, 3.5, 4.0] g/dL). For patients with SARS-CoV-2 seropositivity in July, median serum albumin concentrations were identical to the group without antibodies in January and February. However in the group with seropositivity in July, median albumin concentrations started to drop in March, with the lowest concentrations observed in May (median 3.6 [25th, 75th percentile 3.3, 3.9] g/dL). In any given month, a patient could contribute more than one observation as multiple laboratory draws were possible. For patients seronegative for SARS-CoV-2 in July 2020, N by month were 22395, 23127, 24035, 24612, 25369, 25994, and 26082 from January to July 2020. For patients seronegative for SARS-CoV-2 in July 2020, N by month was 22395, 23127, 24035, 24612, 25369, 25994, and 26082 from January to July 2020. For patients seropositive for SARS-CoV-2 in July 2020, N by month was 1694, 1772, 1949, 2036, 2131, 2259, and 2284 from January to July 2020. *P-value from Wilcoxon rank-sum test comparing the distribution of monthly concentrations by SARS-CoV-2 seropositivity status.

Discussion

In this analysis of laboratory correlates of SARS-CoV-2 seropositivity among patients on dialysis, we find that after accounting for community burden of COVID-19, patients with poorer health status (as reflected by lower serum concentrations of creatinine, albumin, and hemoglobin) have higher likelihood of SARS-CoV-2 antibody. These findings were contrary to our hypothesis, as we had proposed that patients with more robust health status would have a higher likelihood of seropositivity, either due to survivor bias, enhanced ability to mount an antibody response, or lower adherence to social distancing measures. Instead we found that patients with SARS-CoV-2 antibodies also have lower overall health status as measured by laboratory surrogates.

Our findings match those from a recent analysis of risk factors for diagnosis of SARS-CoV-2 as reported to the French national registry of patients on dialysis (REIN) [30]. In this analysis, all patients with consistent symptoms as well as diagnostic rtPCR over a 3-month period were compared to patients without diagnosed SARS-CoV-2 infection. A substantial proportion (~41%) were outpatients with mild or no symptoms. Frail patients—e.g., those requiring assistance to transfer, or those with ischemic heart disease or serum albumin < 3.5 g/dL—experienced higher odds of infection.

Our retrospective analyses suggest one potential explanation for this finding. It has long been known that viral respiratory infections take a metabolic toll, decreasing serum albumin [31-33]. Although SARS-CoV-2 infection is reported to be asymptomatic in up to 40% of patients on dialysis [13], it may nonetheless trigger a significant inflammatory response [34, 35], as evidenced by the drop in serum albumin concentrations among patients with SARS-CoV-2 seropositivity concurrent with the Spring peak of the pandemic in the U.S. In fact, our observed effect may be attenuated because patients who were contemporaneously hospitalized or died from SARS-CoV-2 would have been expected to have the sharpest drop in serum albumin, and would not have been among the patients sampled. This implies that even among the survivors, SARS-CoV-2 has had a significant adverse effect on health status, since patients on dialysis with albumin <3.5 g/dL have a 7-fold higher risk for death, compared with patients with albumin ≥ 4g/dL [11].

Alternatively, in the event of SARS-CoV-2 exposure, contrary to the documented lower likelihood of antibody response after hepatitis B and influenza vaccination [36, 37], patients with poorer health status may be more likely to experience illness associated with an antibody response. Some reports assessing quantitative antibody responses following SARS-CoV-2 infection illustrate that patients without symptoms or with milder disease may be less likely to mount an antibody response, or if a response is present, it may be weaker and of shorter duration [38-40]. In an analysis of 635 serial samples of persons with SARS-CoV-2, 13% of persons who were asymptomatic or remained outpatient did not have evidence of receptor binding domain IgG at four weeks, compared with only 2% of persons hospitalized with SARS-CoV-2 [38]. Furthermore, there was a faster slope of decline in antibody titers among persons with mild or no illness. Thus, it is possible that healthier patients receiving dialysis may have had similar rates of infection, but in the cross-section, due to their milder disease course, less likely to have evidence of antibody.

Other markers associated with higher likelihood of seropisitivity included lower serum sodium concentrations, serum phosphate, and PTH concentrations. A single (baseline) measure of low serum sodium is associated with higher mortality among patients on dialysis [41], and may reflect fluid overload or heart failure—although data on these comorbidities were not available in our analysis. Low serum phosphate and parathyroid hormone concentrations both reflect poorer nutritional status. In their analysis of 622 patients on dialysis followed for over 14 years, Avram et al. found that low PTH concentrations were associated with higher mortality risk, and that a 10-fold increase in PTH was associated with a 27% lower risk for mortality [42]. These findings are confirmed by others among patients on dialysis [42-45], indicating that extremes of PTH and phosphate concentrations also reflect poorer health status among patients on dialysis.

Our analyses are limited by lack of data on rtPCR-proven diagnosis, symptoms, or hospitalizations. Furthermore, we did not have access to data on dialysis prescription, clinical status, comorbidities, nursing home residence or functional status. The associations assessed here are cross-sectional, and further longitudinal analyses are required to definitively determine whether the purported associations are due to, versus predispose to, SARS-CoV-2 infection. Our analysis also has several strengths, including the use of a central laboratory, a well-validated assay for SARS-CoV-2 seropositivity, retrospective data on serum albumin concentrations, and a large sample size with information to account for other factors associated with seropositivity.

In summary, laboratory concentrations consistently associated with poorer health status and higher risk for mortality were also associated with higher likelihood of SARS-CoV-2 antibodies in persons receiving dialysis. Further insights will be gained by examining quantitative and longitudinal patient samples to assess the risk factors for infection, and the strength and duration of antibody response post-infection.

Relations among serum potassium and sodium and SARS-CoV-2 seropositivity.

Serum potassium (Panel A) and sodium (Panel B) concentrations < 5 meq/L and < 140 meq/L respectively were associated with higher odds of seropositivity. For potassium, there was also a higher odds for seropositivity at potassium concentrations above 5.0 meq/L.

(TIF)

Click here for additional data file.

Relations among serum phosphate and PTH, and SARS-CoV-2 seropositivity.

Panels A and B show odds of seropositivity compared with reference concentrations of 5.5 mmol/L and 300 pg/mL for phosphate and PTH respectively.

(TIF)

Click here for additional data file.

Relations among albumin and SARS-CoV-2 seropositivity by age groups.

In evaluating whether age modified the association between albumin and seropositivity, we found that the overall relationship was similar across age categories. Older patients however had higher odds of SARS-CoV-2 seropositivity than younger patients at serum albumin concentrations below 4 g/dL vs. a concentration at 4 g/dL (p value for interaction = 0.0035).

(TIF)

Click here for additional data file.

Relations among creatinine and SARS-CoV-2 seropositivity by age groups.

For serum creatinine, a similar trend holds true, i.e., that overall patients with lower serum creatinine had higher odds for seropositivity, and that older patients had higher odds of SARS-CoV-2 seropositivity than younger patients at serum creatinine concentration 12.5 mg/dL (p value for interaction = 0.0058). However among the 80 years or above category, a small number of persons had serum creatinine concentration 12.5 mg/dL or higher, thereby obscuring this trend.

(TIF)

Click here for additional data file.

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19 Jan 2021

PONE-D-20-36552

Laboratory Correlates of SARS-CoV-2 Seropositivity in a Nationwide Sample of Patients on Dialysis in the U.S.Laboratory Correlates of SARS-CoV-2 Seropositivity in a Nationwide Sample of Patients on Dialysis in the U.S.

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Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

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a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I read with great interest the paper entitled "Laboratory Correlates of SARS-CoV-2 Seropositivity in a Nationwide Sample of Patients on Dialysis in the U.S".

This cross-sectional study aimed to assess the association between SARS-CoV-2 antibodies and seven laboratory (inflammatory and nutritional) parameters in 28,503 dialysis patients. Authors hypothesized that patients with better nutritional laboratory parameters would be able to mount their antibodies, however they found more positive serology in patients with parameters associated with poorer health status.

The research question is clear and limitations of the study were clearly stated at the end.

The strength of this study resides in the large sample and the analysis of serum albumin over several months before the SARS-CoV-2 seropositivity. Obviously, due to the cross-sectional design of the study, one cannot draw causal inference.

I have some comments that could help the authors improve their manuscript, if they can address them in the methodology, discussion and/or limitations.

Major comments:

1-Why did the authors consider only the serum albumin as highly affected by inflammation? Hemoglobin may drop in acute infections and the speed of this drop can vary between an individual and another. It would have been interesting to look at hemoglobin with the same strategy followed for serum albumin between January and July. Same thoughts for potassium and phosphate that can be affected by an acute infection and drop within days if the patient is sick and not eating. How do authors interpret the association between seropositivity and low PTH (this was not addressed in the discussion)? Why did authors choose creatinine and not BUN? Why did authors assume that sodium can be assessed without any clinical data (fluid overload for instance)?

Minor comments: I couldn't find within the manuscript the percentage of hemodialysis versus peritoneal dialysis patients.

Reviewer #2: The authors focused on the relationship laboratory data and COVID-19 seropositivity. This study is a relatively large-scale observational study, and the significance of the obtained knowledge is great. However, I think that the results are not well discussed in manuscript.

My comments are as follows.

Major

1. Before the pandemic, most of the patients were mild status, whereas after the pandemic, there should be many severe cases. Patients with high antibody titers are more likely to have been hospitalized due to severe illness, and their serum albumin is likely to have decreased during the course of treatment for the infection.

2. In Figure 2, the number of patients per graph should be described.

3. There is no difference in serum albumin value before the pandemic between the antibody positive and negative cases, and the serum albumin is low after the pandemic. Since this paper is an observational study, it should be avoided to mention that low albumin is associated with mortality.

4. Elevated antibody titers are the result of infection, and health before infection is important. Comparing positive and negative antibody titers does not directly compare the risk of infection.

Minor

1. The author should describe what kind of statistical analysis was done for the content stated in the last sentence of Result section.

2. There are BCG method, BCP method and BCP improvement method as albumin level measurement methods. It should be shown whether all facilities use the same testing method.

Reviewer #3: The study is interesting, and it clearly states the results found in it. However, I have some remarks and recommendations for it.

1. It should state the criteria of the authors to select the cut-off points to make the comparison among the different variables, e.g. creatine 12,5 vs 8 vs 5.

2. Having a wide range of the participants' age would make it possible to perform a sub-analysis with different sublayers to evaluate the behavior of the chosen variables according to the patients' age.

3. It is shown to detail the results of the albumin in May and the difference between the two studied groups. But it is unknown if this variation happens also with the other variables, or it is exclusive to the albumin.

4. The hypotheses to find the variation of the albumin are clearly and meticulously state in the article. Nevertheless, there is no enough information to support the hypothesis for the variation of the other variables.

5. In graph B from Figure 1, the maximum value of the creatinine is missing.

6. In Figure 2, there is a typo mistake, the abbreviation of February is Feb instead of Fev.

7. Evaluating calcium involvement as a variable in the analysis. There several studies that relate it with a mineral metabolic bone disease, and this one with an alteration of the immune system.

8. The Figures that show the behavior of the Sodium and PTH reveal that having high values of them reduces the probability of acquiring antibodies. I think it should be some comments on the results and hypothesis about it.

I really enjoyed reading this article, congratulations to all the authors for such an interesting paper.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Mabel Aoun

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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16 Mar 2021

PONE-D-20-36552

Laboratory Correlates of SARS-CoV-2 Seropositivity in a Nationwide Sample of Patients on Dialysis in the U.S.Laboratory Correlates of SARS-CoV-2 Seropositivity in a Nationwide Sample of Patients on Dialysis in the U.S.

Response to Review

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Response: These have been addressed, and the paper reformatted accordingly.

2. Thank you for including your ethics statement: "Stanford University IRB approved the study."

a. Please amend your current ethics statement to include the full name of the ethics committee/institutional review board(s) that approved your specific study.

b. Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

Response: The statement has been revised to state that the Stanford University Institutional Review Board 61 (Registration 4947) approved this work under study protocol #56901 (Lines 94-99).

3. In your ethics statement in the manuscript and in the online submission form, please ensure that you have discussed whether all data/samples were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data/samples from their medical records used in research, please include this information.

Response: Thank you for the careful review. We added below to our Revised Materials and Methods:

The data were fully anonymized before Stanford University researchers analyzed them; all sample analyses were performed as part of routine clinical care or using plasma that would have otherwise been discarded (for the SARS-CoV-2 antibody testing). The Stanford University IRB waived requirement for informed consent (Lines 94-99).

4. In the ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records/samples used in your retrospective study, including: a) the date range (month and year) during which patients' medical records/samples were accessed; b) the date range (month and year) during which patients whose medical records/samples were selected for this study sought treatment; and c) the source of the medical records/samples analyzed in this work (e.g. hospital, institution or medical center name).

Response: In the requested place we have now updated these data to indicate: All data were anonymized prior to analysis Patients’ electronic health data on demographics and comorbidities as available to Ascend Clinical were accessed for the month of the seroprevalence analysis (July 2020). Patients’ laboratory data—performed as part of routine clinical care—were accessed dating back 6 months prior to seroprevalence testing (January 2020). The source of data were Ascend Clinical laboratory and electronic medical record data (See Revised Methods Lines 107-112).

5. During your revisions, please note that a simple title correction is required: Please ensure the title is entered only once in online submission form, so that it reads "Laboratory Correlates of SARS-CoV-2 Seropositivity in a Nationwide Sample of Patients on Dialysis in the U.S."

Response: This correction has been made.

6. Thank you for stating the following in the Competing Interests section:

"The authors have declared that no competing interests exist."

We note that one or more of the authors are employed by a commercial company: Ascend Clinical Laboratory.

Response: This is correct; thank you again for your careful review. We have updated the Competing interests section to address this (see 6.1).

6.1. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

Response: We updated the funding statement to state clearly that:

Ascend Clinical funded the remainder plasma testing performed for seroprevalence analysis. In addition, authors JB, RK, and PB are employed by Ascend Clinical. JB assisted with data preparation including anonymized electronic health data preparation, RK selected the laboratory assays and supervised sample analysis, and PB co-conceived the study with GMC.

6.2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Response: We have updated the Competing interests statement to the following:

JB, RK and PB are employed by Ascend Clinical Laboratories. GMC is on the Board of Satellite Healthcare, a not-for-profit dialysis organization. This does not alter our adherence to PLoS ONE policies on sharing data and materials.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

Response: We certify that all competing interests have been declared, and we thank PLoS One Editors for helping us to clearly state all competing interests.

7. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

Response: We do not have an objection to making the data publically available, and have prepared a data set for publication on our website, at a timeline suggested by the Editors: https://covidkidney.stanford.edu/

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Response: We have prepared a dataset to be publically available at our website: https://covidkidney.stanford.edu/ . We will await instructions from the Editorial team regarding the timing of its publication.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

________________________________________

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I read with great interest the paper entitled "Laboratory Correlates of SARS-CoV-2 Seropositivity in a Nationwide Sample of Patients on Dialysis in the U.S".

This cross-sectional study aimed to assess the association between SARS-CoV-2 antibodies and seven laboratory (inflammatory and nutritional) parameters in 28,503 dialysis patients. Authors hypothesized that patients with better nutritional laboratory parameters would be able to mount their antibodies, however they found more positive serology in patients with parameters associated with poorer health status.

The research question is clear and limitations of the study were clearly stated at the end.

The strength of this study resides in the large sample and the analysis of serum albumin over several months before the SARS-CoV-2 seropositivity. Obviously, due to the cross-sectional design of the study, one cannot draw causal inference.

I have some comments that could help the authors improve their manuscript, if they can address them in the methodology, discussion and/or limitations.

Major comments:

1-Why did the authors consider only the serum albumin as highly affected by inflammation? Hemoglobin may drop in acute infections and the speed of this drop can vary between an individual and another. It would have been interesting to look at hemoglobin with the same strategy followed for serum albumin between January and July. Same thoughts for potassium and phosphate that can be affected by an acute infection and drop within days if the patient is sick and not eating.

Response: We thank the Reviewer for his or her careful and overall positive review. We agree with the Reviewer that in this cross-sectional analysis, it is hard to disentangle factors that could be responsible for the findings. As astutely implied by the Reviewer, it is possible that patients’ with SARS-CoV-2 infection and resultant seropositivity had a decline in concentrations of albumin, hemoglobin, potassium, and/or phosphate and that concentrations of albumin, hemoglobin, potassium, and/or phosphate played no role in developing COVID-19 or mounting an antibody response to SARS-CoV-2.

In our discussion (paragraphs 3 and 4, lines 237-259) we posit that either scenario could exist to explain our observed associations. Our limited retrospective analyses using a selected marker (i.e., serum albumin) seem to support the first (i.e., a decline in serum albumin occurred around the time of illness). We selected serum albumin as the primary marker of health status in our analyses due to: 1) its likely variation in response to infection and inflammatory response, and 2) its strong and consistently observed inverse association with mortality among patients on dialysis. The other markers—e.g., phosphate or parathyroid hormone—have been observed to have a ‘U’ shaped relationship with mortality among patients on dialysis (Block et al. JASN 2004) . We added this explanation and appropriate references to our methods to strengthen our rationale for selecting serum albumin (see Revised Materials and Methods line 130-132).

Rather than perform further retrospective analyses to explain this cross-sectional association, we propose (and are carrying out) a prospective study to evaluate whether SARS-CoV-2 illness has significant effects on the health status of patients on dialysis.

2. How do authors interpret the association between seropositivity and low PTH (this was not addressed in the discussion)? Why did authors choose creatinine and not BUN? Why did authors assume that sodium can be assessed without any clinical data (fluid overload for instance)?

Response: Many years ago, Avram and others (AJKD 1996 and 2001) highlighted the association between parathyroid hormone concentrations and nutritional status in patients receiving dialysis. Although PTH can be influenced by many factors, including serum concentrations of calcium and phosphate and the provision of calcitriol or active vitamin D analogs and calcimimetics, nutritional status continues to play a role. We added this explanation and appropriate references to our Revised Discussion lines 264-273. Lower serum creatinine concentrations are consistently associated with mortality in the dialysis population, most likely related to muscle wasting in the setting of little or no residual kidney function. We elected to include serum creatinine as a marker of muscle mass and quality, not kidney function (as would be the case in a general population cohort). Hoping to limit the inquiry to just a handful of laboratory tests (for a more parsimonious presentation), we did not explore associations with BUN. In patients receiving dialysis, the urea nitrogen concentration is confounded by dietary protein intake, the type and integrity of vascular access, and other factors.

On the Reviewer’s last comment on serum sodium, we agree that interpretation of our analyses in general requires caution as we do not have detailed clinical data. Several observational studies have however demonstrated higher mortality among patients on dialysis with ‘baseline’ (single measure) low serum sodium (Sun et al. Scientific Reports 2017). We added a systematic review of these studies as a reference to our Revised Discussion lines 264-273, and have also added the limitation of clinical data to our Revised Discussion line 275 .

3. Minor comments: I couldn't find within the manuscript the percentage of hemodialysis versus peritoneal dialysis patients.

Response: We did not have direct information on modality, but defined a participant as being on home modality if he/she had measures of weekly total Kt/v—as would be expected for patients on peritoneal dialysis. We have added these data to Table 1. We thank the Reviewer for his or her close reading.

Reviewer #2: The authors focused on the relationship laboratory data and COVID-19 seropositivity. This study is a relatively large-scale observational study, and the significance of the obtained knowledge is great. However, I think that the results are not well discussed in manuscript.

My comments are as follows.

Major

1. Before the pandemic, most of the patients were mild status, whereas after the pandemic, there should be many severe cases. Patients with high antibody titers are more likely to have been hospitalized due to severe illness, and their serum albumin is likely to have decreased during the course of treatment for the infection.

Response: We agree with the Reviewer that this is one possible explanation for our observed association between serum albumin and SARS-CoV-2 seropositivity. See our text further discussing this potential implication (lines 241-263; 276-278).

2. In Figure 2, the number of patients per graph should be described.

Response: We thank the Reviewer for his or her close review. Since the figure already had significant data, we added these data to the Figure legend.

3. There is no difference in serum albumin value before the pandemic between the antibody positive and negative cases, and the serum albumin is low after the pandemic. Since this paper is an observational study, it should be avoided to mention that low albumin is associated with mortality.

Response: We agree and do not claim any inference between albumin and mortality on the basis of our current analyses. However we have cited in Materials and Methods (lines 130-131) prior studies demonstrating this association, to explain our rationale for selecting serum albumin as a chief marker of interest.

4. Elevated antibody titers are the result of infection, and health before infection is important. Comparing positive and negative antibody titers does not directly compare the risk of infection.

Response: We agree, please see our response to your comment 1 and Reviewer 1’s comment 1.

Minor

1. The author should describe what kind of statistical analysis was done for the content stated in the last sentence of Result section.

Response: We used the using Wilcoxon rank-sum test to compare the distributions, and this is stated in the methods line149-150.

2. There are BCG method, BCP method and BCP improvement method as albumin level measurement methods. It should be shown whether all facilities use the same testing method.

Response: The tests were all done at a central laboratory, which used the Bromcresol Green (BCG) methods for albumin testing. We have updated these details in our Materials and Methods section. We again thank the Reviewer for his or her close review of our work.

Reviewer #3: The study is interesting, and it clearly states the results found in it. However, I have some remarks and recommendations for it.

1. It should state the criteria of the authors to select the cut-off points to make the comparison among the different variables, e.g. creatine 12,5 vs 8 vs 5.

Response: As a reference for the cut points for several laboratory values, we used the seminal analysis published by Lowrie and Lew entitled ‘Death risk in hemodialysis patients: the predictive value of commonly measured variables and an evaluation of death rate differences between facilities’ and published in the American Journal of Kidney Disease in 1990 which first established the relationships between several laboratory markers (including albumin, creatinine, potassium, and phosphate) and risk for death among patients on dialysis. In the Revised Methods line 144-145 we have made clear that we used cut points on the basis of this reference for four laboratories. References for the remainder laboratories are also stated in the Revised Methods line146-147.

2. Having a wide range of the participants' age would make it possible to perform a sub-analysis with different sublayers to evaluate the behavior of the chosen variables according to the patients' age.

Response: We believe here that the Reviewer is suggesting testing for whether age modifies the association between albumin and seroprevalence for example. We appreciate this suggestion, which is an intriguing perspective on the data. Since our analysis included multiple imputation for missing laboratory correlates, testing for the interaction required us to update the entire analysis. We tested for interaction with the three laboratory correlates featured in our main analysis: albumin, hemoglobin, and creatinine. There was no significant interaction between hemoglobin and age in the association with seroprevalence (p=0.9711).

Creatinine Albumin Hemoglobin

We found that for serum albumin, the overall relationship is similar across age categories. Older patients, however, had higher odds of SARS-CoV-2 seroprevalence than younger patients at serum albumin concentrations below 4 g/dL vs a concentration at 4 g/dL (see figure below; p value for interaction = 0.0035).

For serum creatinine, a similar trend holds true until the age 80 years or above category (see figure below, p value for interaction = 0.0058). The relatively lower muscle mass in patients 80 years or older likely means that the referent serum creatinine level should in fact be lower than 12.5 mg/dL; in fact very few patients over age 80 years old (n=15) had serum creatinine ≥ 12.5 mg/dL. Put in this context, except at the extremes of age groups, the results below continue to support our current conclusion that lower serum creatinine levels are indicative of lower muscle mass and poorer health status in patients on dialysis, and are associated with higher likelihood of SARS-CoV-2 seropositivity.

We can include these data, figures, and their interpretation in the Supplemental material at the Reviewer or Editor’s discretion. We again thank the Reviewer for his or her careful review.

3. It is shown to detail the results of the albumin in May and the difference between the two studied groups. But it is unknown if this variation happens also with the other variables, or it is exclusive to the albumin.

Response: Your comment echoes Reviewer 1’s comment #1. As explained in our response to this comment, we relied on serum albumin as our primary marker of interest due to its consistently observed inverse relation with mortality among patients on dialysis.

4. The hypotheses to find the variation of the albumin are clearly and meticulously state in the article. Nevertheless, there is no enough information to support the hypothesis for the variation of the other variables.

Response: We hope that by adding additional explanations to the observed association for PTH and sodium (see Revised Discussion lines 264-273), we have enriched our discussion as suggested by the Reviewers.

5. In graph B from Figure 1, the maximum value of the creatinine is missing.

Response: The maximum value is 15, and we have fixed this error. Thank you for your close review of our work.

6. In Figure 2, there is a typo mistake, the abbreviation of February is Feb instead of Fev.

Response: Again thank you for this close review. We fixed this typo.

7. Evaluating calcium involvement as a variable in the analysis. There several studies that relate it with a mineral metabolic bone disease, and this one with an alteration of the immune system.

Response: The Reviewer is quite correct that serum calcium is a critical component of the mineral-bone axis in patients on dialysis, which in turn could influence the activity of the immune system. However in this cross sectional analysis we wanted to limit the total number of associations tested, and a priori selected laboratories we hypothesized would have an association with SARS-CoV-2 seropositivity. We had selected two of the three commonly measured mineral bone disease markers (phosphate and PTH), we had not selected a third (calcium). For parsimony, we limited the number of laboratory studies tested to seven. If the Editors feel strongly, we could test calcium, but would prefer to keep the analysis as is.

8. The Figures that show the behavior of the Sodium and PTH reveal that having high values of them reduces the probability of acquiring antibodies. I think it should be some comments on the results and hypothesis about it.

Response: Your comment here is very similar Reviewer 1’s comment 1. We have revised our discussion to include a potential explanation for the observed association (Revised Discussion lines 264-273).

I really enjoyed reading this article, congratulations to all the authors for such an interesting paper.

We thank the Reviewer for his or her time, and for this encouraging comment.

________________________________________

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19 Mar 2021

Laboratory Correlates of SARS-CoV-2 Seropositivity in a Nationwide Sample of Patients on Dialysis in the U.S.

PONE-D-20-36552R1

Dear Dr. Anand,

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6 Apr 2021

PONE-D-20-36552R1

Laboratory correlates of SARS-CoV-2 seropositivity in a nationwide sample of patients on dialysis in the U.S.

Dear Dr. Anand:

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