Introduction
Chronic metabolic acidosis and organic anion accumulation are classically associated with advanced CKD (1,2). However, recent research has revealed clinically actionable (3,4) acid retention and anion accumulation in the absence of frank serum electrolyte abnormalities among patients without advanced CKD (5,6). Treatment of this eubicarbonatemic (7) metabolic acidosis prevents GFR decline (4), reduces markers of kidney tissue damage (3), and in patients without CKD, reduces markers of muscle and bone degradation (8,9).
Evidence of both eubicarbonatemic and overt metabolic acidosis has also been demonstrated among patients without CKD. In a recent retrospective cohort study, greater body mass index (BMI) was associated with progressively higher risk of low serum bicarbonate (10). This suggests that excess weight may be associated with chronic acid retention or overproduction in the absence of CKD. Patients who are overweight and patients with obesity are a growing population worldwide (11) at risk for CKD (12) and ESKD (13). Metabolic acidosis in this population may represent a novel therapeutic target for preventing kidney function decline (3,4).
This study aimed to further define the relationship between obesity and acid-base disturbance (10) by investigating abnormalities of the serum anion gap as an indicator of organic acid retention. Apart from its diagnostic use (14), the anion gap is predictive of clinical outcomes (6,15). In this context, the incorporation of serum albumin, as the corrected anion gap, may improve the sensitivity for detecting clinical abnormalities and disease risk. In contrast to the traditional anion gap, corrected anion gap elevations are detectable in CKD Stage 2 (6), and they have been associated with mortality risk (6) and relatively higher risk of progression to ESKD (15).
Using data from a diverse outpatient population, a prospective analysis was conducted to examine whether (1) greater BMI is associated with higher risk of incident elevated anion gap and (2) higher anion gap values within this population are associated with low bicarbonate and anion gap metabolic acidosis.
Materials and Methods
Study Population
Adult patients (age ≥18 years) visiting outpatient clinics within the Montefiore Medical Center (MMC) health system between January 1, 2010 and December 31, 2015 were eligible for inclusion if they had complete demographic data, two or more outpatient clinic visits during the study period, two or more basic metabolic panels and serum albumins drawn in the outpatient setting, and a baseline plus at least one subsequent BMI value measured over the study period. Patients were additionally required to have visited an MMC clinic at least once during the year prior to the index date. The index date was defined as the first available anion gap value. Patients were excluded if they had an International classification of diseases (ICD-9) diagnosis within 10 years before or 90 days after the index date for asthma or other lung disease, congestive heart failure, pulmonary hypertension, obesity hypoventilation syndrome or obstructive sleep apnea, inflammatory bowel disease, chronic diarrhea, liver disease, ascites, HIV, AIDS, or any malignancy or if they filled a prescription within 90 days of the index date for diuretics, base equivalents, or antiretroviral medication. Patents with baseline bicarbonate values <15 or >40 mEq/L or baseline BMI values <15 or >60 kg/m2 were excluded as outliers (n=636). The study was approved by the institutional review board for MMC/Albert Einstein College of Medicine (institutional review board no. 2018-9100). Informed consent was waived due to the use of deidentified data.
Data Collection
Patient data were obtained from the MMC electronic health record. BMI was calculated as weight in kilograms divided by height in meters squared. Patients who identified as Black Hispanic were included as Hispanic. Patients who did not identify as Black, White, Hispanic, or Asian were a small sample size and were combined with Asian patients in the Asian/other racial and ethnicity category. Baseline comorbidity was defined by the presence of an ICD-9 diagnosis code from 5 years before to 90 days after the index date. Diabetes mellitus was additionally defined by an A1c value ≥6.5% or a prescription for diabetes medication during this index time frame. Income was determined for each patient using the average household income within the patient’s zip code (16).
Only outpatient laboratory values were included in analyses. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration formula (17). Traditional and corrected anion gap values were calculated (6,15,18,19) from serum electrolytes, and albumin was collected during the same blood draw: traditional anion gap = serum sodium (milliequivalents per liter) – [serum chloride (milliequivalents per liter) + serum bicarbonate (milliequivalents per liter)]; corrected anion gap = traditional anion gap + [4.4 – serum albumin (grams per deciliter)] × 2.5. Incident-elevated anion gap was defined as the first of at least two occurrences after the index date of an anion gap >95th percentile of baseline anion gap values for the total cohort. As there is no standard percentile cutoff for elevated anion gap in previous literature (6,15), this value was chosen because it represents a notable deviation from the mean. Such a degree of elevation is perhaps more likely to represent pathology rather than minor variations in serum electrolytes. Within the MMC laboratory, the normal range for traditional anion gap is 7–16 mEq/L, whereas the mean is not defined. Incident low bicarbonate was defined as the first of at least two bicarbonate values ≤23 mEq/L after the index date. Given the variability in cutoffs for defining low bicarbonate in prior observational studies (20,21), this higher value was chosen to capture more events and increase statistical power. Anion gap metabolic acidosis was defined as the first of at least two occurrences of simultaneous low serum bicarbonate and elevated corrected anion gap. All blood samples for serum electrolytes and albumin were delivered immediately to the laboratory or refrigerated at 4°C–8°C until analyzed centrally within MMC by standardized processes. Serum bicarbonate was measured by the phosphoenolpyruvate carboxylase method, which represents total serum carbon dioxide. There were no changes in analytic methods of serum electrolytes or serum albumin during the study period.
Statistical Analyses
Patient characteristics at baseline were compared across BMI categories by ANOVA for continuous variables and chi-squared tests for categorical variables. The association of baseline BMI and anion gap was examined using linear regression models. The association of BMI with anion gap values over time was examined using multilevel mixed effects models specifying random intercepts and incorporating time-updated BMI, eGFR, and anion gap values. The associations of baseline BMI and incident elevated anion gap with anion gap metabolic acidosis, and of baseline anion gap with incident low bicarbonate and anion gap metabolic acidosis, were examined using Cox proportional hazards models. Time 0 was the date of the first available anion gap value during the study period. Patients were censored at the time of the last available anion gap or bicarbonate value. Covariates for all models were selected a priori and included age; sex; racial and ethnicity; eGFR; income; insurance status; and baseline hypertension, diabetes, and coronary artery disease. A P value of 0.05 was considered statistically significant. Data were analyzed using Stata 13.1 (StataCorp, College Station, TX).
Subgroup and Sensitivity Analyses
Analyses stratified by eGFR group were completed to rule out residual confounding by kidney function (6). Racial and ethnicity subgroups were analyzed due to the previously demonstrated racial and ethnic differences in low bicarbonate risk with greater BMI (10). Stratified analyses by hypertension status (22) and diabetes status (23) were completed to explore potential confounding by comorbidity and medication use. Given the association of dietary acid load and anion gap among patients with moderate CKD (15), an analysis incorporating baseline BUN as a surrogate for dietary protein was also included.
Results
Baseline Characteristics
In total, 94,488 and 94,197 patients were included in cross-sectional analyses of traditional and corrected anion gap, respectively. A small number of patients (n=291, 0.3%) were included in the traditional but not corrected cohort analysis, resulting from differences in available laboratory data. The mean age was 50.3±17 years, 64% were women, 34% were Black, 12% were White, and 36% were Hispanic. At baseline, 35% were overweight, and 43% were obese (class 1 or above). Hypertension, diabetes, and coronary artery disease were more common among patients with greater BMI. Traditional and corrected anion gaps were higher among overweight and obese categories compared with normal weight (Table 1).
Table 1. -
Baseline characteristics by World Health Organization categories of body mass index
| Characteristics |
Body Mass Index, kg/m2
|
| <18.5 |
18.5 to <25 |
25 to <30 |
30 to <35 |
35 to <40 |
≥40 |
| Patients, n
|
1,049 |
20,041 |
32,664 |
22,594 |
10,726 |
7,414 |
| Age, yr |
43 (23) |
49 (20) |
52 (17) |
51 (15) |
49 (15) |
46 (14) |
| Women, n (%) |
766 (73) |
13,046 (65) |
19,531 (60) |
14,481 (64) |
7534 (70) |
5538 (75) |
|
Racial and ethnicity, n (%)
|
|
|
|
|
|
|
| Black |
330 (32) |
5804 (29) |
10,535 (32) |
8286 (37) |
4201 (39) |
3132 (42) |
| Hispanic |
316 (30) |
6582 (33) |
11,896 (36) |
8384 (37) |
3885 (36) |
2665 (36) |
| Asian/other race |
247 (24) |
4581 (23) |
6495 (20) |
3705 (16) |
1632 (15) |
1009 (14) |
| White |
156 (15) |
3074 (15) |
3738 (11) |
2219 (10) |
1008 (9) |
608 (8) |
|
Insurance, n (%)
|
|
|
|
|
|
|
| Commercial |
583 (56) |
11,981 (60) |
20,323 (62) |
14,246 (63) |
6763 (63) |
4603 (62) |
| Medicare |
176 (17) |
3333 (17) |
5111 (16) |
3168 (14) |
1299 (12) |
724 (10) |
| Medicaid |
234 (22) |
3703 (19) |
5627 (17) |
4113 (18) |
2144 (20) |
1756 (24) |
| Self-pay |
56 (5) |
1024 (5) |
1603 (5) |
1067 (5) |
520 (5) |
331 (5) |
|
Median income by zip code, n (%)
|
|
|
|
|
|
|
| <$30,000 |
190 (18) |
3525 (18) |
6419 (20) |
4768 (21) |
2413 (23) |
1962 (27) |
| $30,000–$49,999 |
471 (45) |
8824 (44) |
15,104 (46) |
10,698 (47) |
5091 (48) |
3370 (46) |
| $50,000–$69,999 |
163 (16) |
3388 (17) |
5223 (16) |
3404 (15) |
1569 (15) |
927 (13) |
| >$70,000 |
83 (8) |
1684 (8) |
2306 (7) |
1380 (6) |
617 (6) |
366 (5) |
| Not specified |
142 (14) |
2620 (13) |
3612 (11) |
2344 (10) |
1036 (10) |
789 (11) |
|
Baseline diagnoses, n (%)
|
|
|
|
|
|
|
| Hypertension |
258 (25) |
6927 (35) |
15,368 (47) |
12,173 (54) |
6102 (56.9) |
4212 (57) |
| Diabetes |
88 (8) |
3118 (16) |
7618 (23) |
6767 (30.0) |
3642 (34) |
2647 (36) |
| Coronary artery disease |
64 (6) |
1356 (7) |
2689 (8) |
1847 (8.2) |
818 (8) |
484 (7) |
|
Baseline laboratory values
|
|
|
|
|
|
|
| eGFR,
a
ml/min per 1.73 m2
|
101 (30) |
93 (26) |
88 (23) |
89 (23) |
91 (24) |
96 (24) |
| BUN,
b
mg/dl |
14 (6) |
15 (6) |
15 (6) |
15 (6) |
15 (6) |
14 (6) |
| Serum sodium,
c
mEq/L |
140 (3) |
140 (3) |
141 (2) |
141 (2) |
141 (2) |
140 (2) |
| Serum chloride,
c
mEq/L |
103 (3) |
103 (3) |
103 (3) |
103 (3) |
103 (3) |
103 (3) |
| Serum bicarbonate,
c
mEq/L |
25 (3) |
26 (3) |
26 (3) |
26 (3) |
25 (3) |
25 (3) |
| Serum albumin,
d
g/dl (n=94,197) |
4.5 (0.4) |
4.5 (0.4) |
4.5 (0.3) |
4.4 (0.3) |
4.4 (0.3) |
4.2 (0.3) |
|
Anion gap, mEq/L
|
|
|
|
|
|
|
| Traditional |
13 (2.8) |
12.0 (2.6) |
12.2 (2.6) |
12.3 (2.6) |
12.4 (2.6) |
12.4 (2.6) |
| Corrected (n=94,197) |
11.1 (2.8) |
10.9 (2.6) |
11.0 (2.5) |
11.2 (2.5) |
11.5 (2.5) |
11.8 (2.6) |
Continuous variables are reported as means (SD).
aCalculated using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation.
bConversion to Système International units (millimoles per liter): milligrams per deciliter × 0.357= millimoles per liter.
cConversion to Système International units (millimoles per liter): milliequivalents per liter = millimoles per liter.
dConversion to Système International units (grams per liter): grams per deciliter × 0.1= grams per liter.
Association of BMI with Anion Gap
After multivariable adjustment, a higher BMI was associated with progressively greater traditional and corrected anion gap, with a greater effect size seen for the latter (Supplemental Figure 1, A and B). A slight attenuation of effect was seen at the highest BMI values for traditional (Supplemental Figure 1A) but not corrected anion gap (Supplemental Figure 1B). Analyses using more granular categories of BMI showed these same relationships (Supplemental Figure 1, C and D). In particular, there was a striking, graded association of higher BMI with greater corrected anion gap throughout the range of overweight and obesity. Longitudinal associations of BMI with each anion gap measurement were next examined. During a median follow-up period of 4.4 years (interquartile range [IQR], 2.3–6.3), patients had a median of five (IQR, three to eight) traditional and three (IQR, two to six) corrected anion gap measurements. When examining repeated measures over time, positive associations between BMI and both anion gap definitions were again observed (Figure 1, A and B). Using more granular categories of BMI, the same strong graded association was seen with both anion gap definitions. Compared with BMI values of 18.5 to <22.5 kg/m2, higher traditional and corrected anion gaps were seen even among patients with a BMI of 25 to <27.5 kg/m2 (Figure 1, C and D). Although differences in corrected anion gap could be driven by differences in serum albumin associated with obesity (24), results were consistent across quartiles of baseline serum albumin (Supplemental Figure 2).
;)
Figure 1.: Association of body mass index (BMI) with traditional and corrected anion gap over time. Association of time-updated BMI with traditional (A and C; n=94,488) and corrected (B and D; n=94,197) anion gap over time according to World Health Organization (WHO) BMI groups (A and B) and smaller BMI categories (C and D). Models were adjusted for age, sex, racial and ethnicity, baseline income, insurance status, eGFR, hypertension, diabetes, and coronary artery disease. Bars denote 95% confidence intervals. *P<0.001; # P=0.01.
Association of BMI with Risk of Incident Elevated Anion Gap
Traditional and corrected anion gap values above the 95th percentiles of baseline values were defined as >17.0 and >15.8 mEq/L, respectively. A total of 90,459 and 91,494 patients did not have elevated traditional and corrected anion gap at baseline and were included in these analyses; 13,972 (15.4%) and 15,477 (16.9%) patients developed at least two occurrences of elevated traditional and corrected anion gap, respectively, during the follow-up period. Higher baseline BMI was associated with a progressively greater risk of incident elevated traditional and corrected anion gap (Figure 2, A and B). Again, the effect size was greater for corrected compared with traditional. These associations were confirmed with analyses using more granular categories of BMI (Figure 2, C and D). Compared with patients with a normal BMI, those with a BMI>40 had hazard ratios (HRs) of 1.32 (95% confidence interval [95% CI], 1.23 to 1.42) and 1.74 (95% CI, 1.63 to 1.85) for traditional (Figure 2A) and corrected (Figure 2B) anion gap, respectively. Those with a BMI>45 had HRs of 1.32 (95% CI, 1.18 to 1.48) and 1.90 (95% CI, 1.73 to 2.10) for elevated traditional (Figure 2C) and corrected (Figure 2D) anion gap, respectively. We again observed a J-shaped association of BMI and anion gap (Figure 2), a slight attenuation of effect for traditional anion gap at the highest BMI values (Figure 2, A and C), and a dose-response association of BMI and corrected gap, continuing through the highest categories of BMI (Figure 2D).
Figure 2.: Association of BMI with incident elevated anion gap. Hazard ratios for incident elevated traditional (A and C; n=90,459) and corrected (B and D; n=91,494) anion gap according to WHO BMI groups (A and B) and smaller BMI categories (C and D). Models were adjusted for age, sex, racial and ethnicity, baseline income, insurance status, eGFR, hypertension, diabetes, and coronary artery disease. Bars denote 95% confidence intervals. AG, anion gap. *P<0.001; † P=0.05; # P=0.01.
Associations of Obesity and Baseline Anion Gap with Risk of Incident Anion Gap Metabolic Acidosis
Next, to examine whether higher BMI was associated not only with risk of developing an elevated anion gap but also, with risk of anion gap metabolic acidosis, we examined the association of BMI with incident co-occurring (simultaneous) elevated corrected anion gap and low bicarbonate. Over the study period, 7007 (9.3%) of 75,047 patients who did not have either abnormality at baseline developed this outcome. BMI was associated, in a graded fashion, with higher risk (Figure 3). Compared with a normal BMI, values of 30 to <35 and >40 at baseline were associated with HRs of 1.38 (95% CI, 1.26 to 1.50) and 1.53 (95% CI, 1.39 to 1.69), respectively (Figure 3A). Point estimates were similar and remained significant after exclusion of patients with an eGFR<90 ml/min per 1.73 m2 (Figure 3B).
Figure 3.: Association of BMI with incident anion gap metabolic acidosis. Hazard ratios for anion gap metabolic acidosis for the total cohort (A; n=75,047) and those with eGFR≥90 ml/min per 1.73 m2 (B; n=36,902) among those with normal bicarbonate and normal corrected anion gap at baseline. Models were adjusted for age, sex, racial and ethnicity, baseline income, insurance status, eGFR, hypertension, diabetes, and coronary artery disease. Bars denote 95% confidence intervals. *P<0.001.
Finally, we hypothesized that small relative differences of the anion gap represent a state of subclinical anion gap metabolic acidosis and thus, precede the development of more overt disturbances. Among patients with a BMI >30 kg/m2 and normal serum bicarbonate at baseline, higher anion gap measurements were independently associated, in a graded fashion, with higher risk of low bicarbonate. The HRs of the highest compared with lowest quartiles of traditional and corrected anion gap were 1.29 (95% CI, 1.23 to 1.40) and 1.36 (95% CI, 1.26 to 1.48), respectively (Figure 4A). Similar results were obtained for patients without obesity (BMI<30; HRs for highest compared with lowest quartiles, 1.31 [95% CI, 1.23 to 1.38] for traditional and 1.42 [95% CI, 1.34 to 1.50] for corrected anion gap). We also examined the association of baseline corrected anion gap with incident anion gap metabolic acidosis. Among patients with obesity and without these abnormalities at baseline, a similar, graded association was seen across baseline quartiles of corrected anion gap. The HRs of the third and fourth quartiles compared with the first were 1.48 (95% CI, 1.34 to 1.63) and 1.78 (95% CI, 1.59 to 1.99) (Figure 4B).
Figure 4.: Association of baseline anion gap quartile with incident low bicarbonate and anion gap metabolic acidosis. (A) Hazard ratios for incident low bicarbonate according to baseline quartiles of anion gap among patients with obesity (BMI≥30) and normal bicarbonate at baseline (n=32,500 for traditional and n=26,637 for corrected). (B) Hazard ratios for anion gap metabolic acidosis among patients with obesity, normal bicarbonate, and normal corrected anion gap at baseline (n=25,838). Models were adjusted for age, sex, racial and ethnicity, baseline income, insurance status, eGFR, hypertension, diabetes, and coronary artery disease. Bars denote 95% confidence intervals. HR, hazard ratio. *P<0.001; † P=0.05; # P=0.01.
Subgroup and Sensitivity Analyses
Significant positive associations of greater BMI and incident elevated anion gap were seen across eGFR subgroups (Table 2). Risk was also observed across race and ethnic groups (Supplemental Table 1). Exclusion of patients with hypertension at baseline (Supplemental Figure 3) yielded similar, significant point estimates in comparison with the total cohort for risk of incident elevated traditional (Supplemental Figure 3A) and corrected (Supplemental Figure 3B) anion gap. Greater BMI was associated with greater risk of elevated corrected anion gap among patients with (Supplemental Figure 4A) and without (Supplemental Figure 4B) diabetes mellitus at baseline. There was also a similar risk of anion gap metabolic acidosis among patients without diabetes mellitus (Supplemental Table 2) in comparison with the total cohort (Figure 3A). After additional adjustment for baseline BUN, the relationship between BMI and elevated traditional (Supplemental Figure 5A) and corrected (Supplemental Figure 5B) anion gap was similar to analyses without this variable (Figure 2, A and B). Changing the criteria for anion gap metabolic acidosis to include a bicarbonate cutoff of <22 mEq/L, rather than ≤23 mEq/L, also demonstrated a graded and significant, although slightly attenuated, risk of anion gap metabolic acidosis with higher BMI values (Supplemental Table 3). Lastly, mixed effects models were created that included baseline bicarbonate or corrected anion gap as a covariate in order to examine the average change in these variables by BMI group over the study period (Supplemental Table 4). These analyses demonstrated that greater BMI was associated with a greater rate of decline in serum bicarbonate and a greater increase in corrected anion gap.
Table 2. -
Association of body mass index with incident elevated traditional and corrected anion gap among eGFR subgroups
| eGFR, ml/min per 1.73 m2
|
Elevated Traditional Anion Gap |
Elevated Corrected Anion Gap |
| Hazard Ratio (95% Confidence Interval) |
P Value |
Hazard Ratio (95% Confidence Interval) |
P Value |
|
>120 (subgroup size: traditional, n=10,188; corrected, n=10,174)
|
| BMI <18.5 kg/m2
|
1.79 (1.20 to 2.66) |
0.01 |
1.81 (1.25 to 2.62) |
0.01 |
| BMI=18.5 to <25 kg/m2
|
ref |
n/a |
ref |
n/a |
| BMI=25 to <30 kg/m2
|
1.06 (0.87 to 1.30) |
0.56 |
0.89 (0.73 to 1.08) |
0.24 |
| BMI=30 to <35 kg/m2
|
1.00 (0.81 to 1.24) |
0.99 |
1.09 (0.89 to 1.34) |
0.39 |
| BMI=35 to <40 kg/m2
|
1.27 (1.00 to 1.61) |
0.05 |
1.32 (1.06 to 1.65) |
0.05 |
| BMI >40 kg/m2
|
1.10 (0.86 to 1.41) |
0.43 |
1.43 (1.15 to 1.78) |
0.01 |
|
90 to <120 (subgroup size: traditional, n=35,770; corrected, n=36,163)
|
| BMI <18.5 kg/m2
|
1.62 (1.16 to 2.25) |
0.01 |
1.70 (1.26 to 2.32) |
0.01 |
| BMI=18.5 to <25 kg/m2
|
ref |
n/a |
ref |
n/a |
| BMI=25 to <30 kg/m2
|
1.15 (1.05 to 1.26) |
0.01 |
1.16 (1.06 to 1.26) |
0.01 |
| BMI=30 to <35 kg/m2
|
1.30 (1.18 to 1.43) |
<0.001 |
1.44 (1.31 to 1.58) |
<0.001 |
| BMI=35 to <40 kg/m2
|
1.35 (1.21 to 1.51) |
<0.001 |
1.56 (1.42 to 1.73) |
<0.001 |
| BMI >40 kg/m2
|
1.41 (1.25 to 1.59) |
<0.001 |
1.88 (1.69 to 2.10) |
<0.001 |
|
60 to <90 (subgroup size: traditional, n=35,482; corrected, n=36,010)
|
| BMI <18.5 kg/m2
|
1.30 (0.91 to 1.85) |
0.15 |
1.50 (1.09 to 2.07) |
0.05 |
| BMI=18.5 to <25 kg/m2
|
ref |
n/a |
ref |
n/a |
| BMI=25 to <30 kg/m2
|
1.04 (0.96 to 1.12) |
0.38 |
1.11 (1.03 to 1.20) |
0.01 |
| BMI=30 to <35 kg/m2
|
1.15 (1.06 to 1.25) |
0.01 |
1.38 (1.28 to 1.49) |
<0.001 |
| BMI=35 to <40 kg/m2
|
1.27 (1.15 to 1.40) |
<0.001 |
1.58 (1.44 to 1.74) |
<0.001 |
| BMI >40 kg/m2
|
1.29 (1.14 to 1.45) |
<0.001 |
1.76 (1.58 to 1.97) |
<0.001 |
|
30 to <60 (subgroup size: traditional, n=8397; corrected, n=8525)
|
| BMI <18.5 kg/m2
|
0.99 [0.54 to 1.80] |
0.97 |
0.75 [0.40 to 1.41) |
0.38 |
| BMI=18.5 to <25 kg/m2
|
ref |
n/a |
ref |
n/a |
| BMI=25 to <30 kg/m2
|
0.98 (0.87 to 1.11) |
0.75 |
1.01 (0.89 to 1.13) |
0.91 |
| BMI=30 to <35 kg/m2
|
1.04 (0.91 to 1.18) |
0.56 |
1.13 (0.99 to 1.28) |
0.07 |
| BMI=35 to <40 kg/m2
|
1.03 (0.88 to 1.20) |
0.72 |
1.15 (0.98 to 1.34) |
0.08 |
| BMI >40 kg/m2
|
1.18 (0.98 to 1.43) |
0.09 |
1.49 (1.24 to 1.79) |
<0.001 |
|
15 to <30 (subgroup size: traditional, n=622; corrected, n=622)
|
| BMI <18.5 kg/m2
|
1.41 (0.43 to 4.61) |
0.57 |
Too few observations |
n/a |
| BMI=18.5 to <25 kg/m2
|
ref |
n/a |
ref |
n/a |
| BMI=25 to <30 kg/m2
|
1.17 (0.81 to 1.68) |
0.40 |
1.13 (0.78 to 1.65) |
0.52 |
| BMI=30 to <35 kg/m2
|
0.83 (0.57 to 1.20) |
0.32 |
0.83 (0.57 to 1.22) |
0.35 |
| BMI=35 to <40 kg/m2
|
0.84 (0.52 to 1.35) |
0.47 |
1.00 (0.62 to 1.61) |
>0.99 |
| BMI >40 kg/m2
|
0.70 (0.42 to 1.18) |
0.18 |
1.05 (0.62 to 1.77) |
0.86 |
The multivariable model was adjusted for age, sex, race/ethnicity, baseline income, insurance status, hypertension, diabetes, and coronary artery disease. BMI, body mass index; 95% CI, 95% confidence interval; ref, reference; n/a, not applicable.
Discussion
The results of this study demonstrate that higher BMI is associated with greater risk of developing anion gap elevation and anion gap metabolic acidosis. Additionally, among patients with obesity, even small elevations of the anion gap were associated with higher risk of low bicarbonate and more markedly with higher risk of anion gap metabolic acidosis. Collectively, these findings suggest that patients with obesity may be at risk for both subclinical and overt anion gap metabolic acidosis, a significant CKD risk factor (20,25).
The association between BMI and anion gap was seen across eGFR and race and ethnic subgroups; similar among those with and without diabetes; and unchanged after adjustment for BUN, a surrogate for dietary protein as a source of organic acid (15,26). We observed a consistent risk of elevated anion gap in association with obesity across a range of eGFR categories, including in patients with an eGFR of 30 to <60 ml/min per 1.73 m2. Our outcome measurements relied upon at least two occurrences of these abnormalities, suggesting that our findings represent a chronic process with potentially significant clinical implications.
A potential source of anions and metabolic acidosis among patients with obesity is endogenous intermediary metabolites and acid production resulting from mitochondrial dysfunction (27). Relative elevations in fasting and postprandial venous and arterial lactate are described widely in patients with obesity (23,28,29) and insulin resistance (23,30,31). Although lactate is a ubiquitous energy substrate and a base equivalent (32) that is shuttled across cell walls and reutilized throughout the body (33), its capacity to act as a base depends upon its reuse for gluconeogenesis or oxidation within mitochondria (34). Patients with obesity and associated metabolic disease are shown to have reduced mitochondrial number (35), protein content (36), and oxidative capacity (36–38) in multiple metabolically active organs, including skeletal muscle (35), adipose tissue (36), and the liver (38). In this way, acid overproduction due to a pathologic reliance upon nonoxidative metabolism may explain our present and prior (10) findings. Other metabolites apart from lactate may also contribute to the observed anion gap elevations. Studies show small (typically <0.2 mmol) elevations in serum pyruvate (39), succinate (40), and other intermediary metabolites (41) as well as potentially diet-independent elevations of glutamate (39), aspartate (39), and anionic amino acid metabolites (42) among patients with obesity compared with healthy controls.
Limitations of this study include that our findings may be due to undiagnosed obesity-related comorbidities (23,30,31), which perhaps mediate the relationship of BMI with these outcomes. Furthermore, despite our exclusion criteria, we cannot rule out confounding from diseases unrelated to obesity influencing our outcomes. For this large dataset, arterial pH measurements were unavailable; therefore, we were unable to confirm the presence of metabolic acidosis. Early in the course of CKD, normal anion gap metabolic acidosis may also be present, which typically precedes anion gap elevations in more advanced disease (43). We suspect that this sequence of events has perhaps attenuated the relationship between BMI and anion gap metabolic acidosis in our results. Additionally, the anion gap is a relatively insensitive diagnostic tool for detecting lactate elevations (44), which may be due to concurrent abnormalities in sodium and other electrolytes masking anion accumulation determined indirectly by calculation. Given that lactate may be a contributor to our findings (23,28,29), our results perhaps underestimate the risk of anion gap elevation associated with obesity. Moreover, although we have demonstrated that greater obesity is associated with greater increases in anion gap over time, these higher levels may still remain within the normal range. Therefore, we may have underestimated the association of obesity with the risk of elevated anion gap, and a longer period of follow-up might have captured a stronger effect size. Mild corrected anion gap elevations (6) and tissue acid retention (45) have been demonstrated in CKD stage 2, which may also influence our results. However, our findings were consistent across eGFR subgroups, and anion gap metabolic acidosis risk with greater BMI was demonstrated even among patients with an eGFR ≥90 ml/min per 1.73 m2. Certain medications commonly prescribed for patients with diabetes may influence bicarbonate levels among patients with CKD (46). However, our subgroup analysis excluding baseline diabetes mellitus demonstrated a similar risk of anion gap metabolic acidosis as compared with the total cohort. There may also be residual confounding by respiratory alkalosis with renal compensation among patients within this population. We suspect this is a relatively minor contributor to our findings on the basis of the following evidence. Obesity is more commonly associated with hypo- rather than hyperventilation syndromes (47). Also, greater BMI has been shown to associate with higher, not lower, partial pressure of carbon dioxide in arterial blood levels (48) and lower urine pH (49). Collectively, this suggests a metabolic rather than respiratory cause of low bicarbonate in patients with obesity. The association of low bicarbonate with elevated anion gap further suggests a metabolic cause. Nevertheless, we cannot exclude the possibility that in a subset of patients, the association of higher BMI with lower serum bicarbonate represents respiratory alkalosis. A final limitation is that observational data can only demonstrate correlation and not causation. Future studies are needed to confirm these findings and further define acid-base changes in the setting of obesity.
In conclusion, these results demonstrate that an increasingly populous patient group within the United States (50), those who are overweight and those with obesity, may be at risk of anion gap metabolic acidosis, even prior to the development of CKD. This pathology may be of clinical significance given the risk of adverse kidney disease outcomes among patients with obesity (12).
Disclosures
M.K. Abramowitz reports consultancy agreements with Tricida, Inc.; ownership interest in Aethlon Medical, Inc.; and honoraria from Medscape and the National Kidney Foundation. The remaining author has nothing to disclose.
Funding
None.
Author Contributions
M.K. Abramowitz and D.C. Lambert conceptualized the study; D.C. Lambert was responsible for data curation; D.C. Lambert was responsible for formal analysis; M.K. Abramowitz was responsible for methodology; D.C. Lambert was responsible for project administration; M.K. Abramowitz provided supervision; D.C. Lambert wrote the original draft; and M.K. Abramowitz and D.C. Lambert reviewed and edited the manuscript.
Supplemental Material
This article contains the following supplemental material online at http://kidney360.asnjournals.org/lookup/suppl/doi:10.34067/KID.0003562021/-/DCSupplemental.
Supplemental Figure 1. Association of BMI with traditional and corrected anion gap.
Supplemental Figure 2. Association of BMI with corrected anion gap over time in subgroups by albumin quartiles.
Supplemental Figure 3. Association of BMI with incident elevated anion gap among patients without hypertension.
Supplemental Figure 4. Association of BMI with incident elevated corrected anion gap among diabetes subgroups.
Supplemental Figure 5. Association of BMI with incident elevated anion gap adjusted for BUN.
Supplemental Table 1. Association of BMI with incident elevated traditional and corrected anion gap among racial and ethnic subgroups.
Supplemental Table 2. Association of BMI with incident anion gap metabolic acidosis among subgroup without diabetes.
Supplemental Table 3. Association of BMI with incident anion gap metabolic acidosis (bicarbonate <22 mEq/L).
Supplemental Table 4. Association of BMI with change in bicarbonate and corrected anion gap over the study period.
References
1. Moranne O, Froissart M, Rossert J, Gauci C, Boffa JJ, Haymann JP, M’rad MB, Jacquot C, Houillier P, Stengel B, Fouqueray B; NephroTest Study Group: Timing of onset of CKD-related metabolic complications. J Am Soc Nephrol 20: 164–171, 2009
https://doi.org/10.1681/ASN.2008020159
2. Hakim RM, Lazarus JM: Biochemical parameters in chronic renal failure. Am J Kidney Dis 11: 238–247, 1988
https://doi.org/10.1016/S0272-6386(88)80156-2
3. Goraya N, Simoni J, Jo C, Wesson DE: Dietary acid reduction with fruits and vegetables or bicarbonate attenuates kidney injury in patients with a moderately reduced glomerular filtration rate due to hypertensive nephropathy. Kidney Int 81: 86–93, 2012
https://doi.org/10.1038/ki.2011.313
4. Goraya N, Simoni J, Jo CH, Wesson DE: Treatment of metabolic acidosis in patients with stage 3 chronic kidney disease with fruits and vegetables or oral bicarbonate reduces urine angiotensinogen and preserves glomerular filtration rate. Kidney Int 86: 1031–1038, 2014
https://doi.org/10.1038/ki.2014.83
5. Gianella FG, Prado VE, Poindexter JR, Adams-Huet B, Li X, Miller RT, Sakhaee K, Maalouf NM, Moe OW: Spot urinary citrate-to-creatinine ratio is a marker for acid-base status in chronic kidney disease. Kidney Int 99: 208–217, 2021
https://doi.org/10.1016/j.kint.2020.07.006
6. Abramowitz MK, Hostetter TH, Melamed ML: The serum anion gap is altered in early kidney disease and associates with mortality. Kidney Int 82: 701–709, 2012
https://doi.org/10.1038/ki.2012.196
7. Alpern RJ, Sakhaee K: The clinical spectrum of chronic metabolic acidosis: Homeostatic mechanisms produce significant morbidity. Am J Kidney Dis 29: 291–302, 1997
https://doi.org/10.1016/S0272-6386(97)90045-7
8. Frassetto L, Morris Jr. RC, Sebastian A: Potassium bicarbonate reduces urinary nitrogen excretion in postmenopausal women. J Clin Endocrinol Metab 82: 254–259, 1997
https://doi.org/10.1210/jcem.82.1.3663
9. Sebastian A, Harris ST, Ottaway JH, Todd KM, Morris RC: Improved mineral balance and skeletal metabolism in postmenopausal women treated with potassium bicarbonate. N Engl J Med 330: 1776–1781, 1994
https://doi.org/10.1056/NEJM199406233302502
10. Lambert DC, Abramowitz MK: Obesity and the risk of low bicarbonate: A cohort study. Kidney Med 3: 498–506.e1, 2021
https://doi.org/10.1016/j.xkme.2021.02.006
11. Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, Marczak L, Mokdad AH, Moradi-Lakeh M, Naghavi M, Salama JS, Vos T, Abate KH, Abbafati C, Ahmed MB, Al-Aly Z, Alkerwi A, Al-Raddadi R, Amare AT, Amberbir A, Amegah AK, Amini E, Amrock SM, Anjana RM, Ärnlöv J, Asayesh H, Banerjee A, Barac A, Baye E, Bennett DA, Beyene AS, Biadgilign S, Biryukov S, Bjertness E, Boneya DJ, Campos-Nonato I, Carrero JJ, Cecilio P, Cercy K, Ciobanu LG, Cornaby L, Damtew SA, Dandona L, Dandona R, Dharmaratne SD, Duncan BB, Eshrati B, Esteghamati A, Feigin VL, Fernandes JC, Fürst T, Gebrehiwot TT, Gold A, Gona PN, Goto A, Habtewold TD, Hadush KT, Hafezi-Nejad N, Hay SI, Horino M, Islami F, Kamal R, Kasaeian A, Katikireddi SV, Kengne AP, Kesavachandran CN, Khader YS, Khang YH, Khubchandani J, Kim D, Kim YJ, Kinfu Y, Kosen S, Ku T, Defo BK, Kumar GA, Larson HJ, Leinsalu M, Liang X, Lim SS, Liu P, Lopez AD, Lozano R, Majeed A, Malekzadeh R, Malta DC, Mazidi M, McAlinden C, McGarvey ST, Mengistu DT, Mensah GA, Mensink GBM, Mezgebe HB, Mirrakhimov EM, Mueller UO, Noubiap JJ, Obermeyer CM, Ogbo FA, Owolabi MO, Patton GC, Pourmalek F, Qorbani M, Rafay A, Rai RK, Ranabhat CL, Reinig N, Safiri S, Salomon JA, Sanabria JR, Santos IS, Sartorius B, Sawhney M, Schmidhuber J, Schutte AE, Schmidt MI, Sepanlou SG, Shamsizadeh M, Sheikhbahaei S, Shin MJ, Shiri R, Shiue I, Roba HS, Silva DAS, Silverberg JI, Singh JA, Stranges S, Swaminathan S, Tabarés-Seisdedos R, Tadese F, Tedla BA, Tegegne BS, Terkawi AS, Thakur JS, Tonelli M, Topor-Madry R, Tyrovolas S, Ukwaja KN, Uthman OA, Vaezghasemi M, Vasankari T, Vlassov VV, Vollset SE, Weiderpass E, Werdecker A, Wesana J, Westerman R, Yano Y, Yonemoto N, Yonga G, Zaidi Z, Zenebe ZM, Zipkin B, Murray CJL; GBD 2015 Obesity Collaborators: Health effects of
overweight and obesity in 195 countries over 25 years. N Engl J Med 377: 13–27, 2017
https://doi.org/10.1056/NEJMoa1614362
12. Chang Y, Ryu S, Choi Y, Zhang Y, Cho J, Kwon MJ, Hyun YY, Lee KB, Kim H, Jung HS, Yun KE, Ahn J, Rampal S, Zhao D, Suh BS, Chung EC, Shin H, Pastor-Barriuso R, Guallar E: Metabolically healthy obesity and development of chronic kidney disease: A cohort study. Ann Intern Med 164: 305–312, 2016
https://doi.org/10.7326/M15-1323
13. Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS: Body mass index and risk for end-stage renal disease. Ann Intern Med 144: 21–28, 2006
https://doi.org/10.7326/0003-4819-144-1-200601030-00006
14. Kraut JA, Nagami GT: The serum anion gap in the evaluation of acid-base disorders: What are its limitations and can its effectiveness be improved? Clin J Am Soc Nephrol 8: 2018–2024, 2013
https://doi.org/10.2215/CJN.04040413
15. Banerjee T, Crews DC, Wesson DE, McCulloch CE, Johansen KL, Saydah S, Rios Burrows N, Saran R, Gillespie B, Bragg-Gresham J, Powe NR: Elevated serum anion gap in adults with moderate chronic kidney disease increases risk for progression to end-stage renal disease. Am J Physiol Renal Physiol 316: F1244–F1253, 2019
https://doi.org/10.1152/ajprenal.00496.2018
17. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro 3rd AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration): A new equation to estimate glomerular filtration rate. Ann Intern Med 150: 604–612, 2009
https://doi.org/10.7326/0003-4819-150-9-200905050-00006
18. Kraut JA, Madias NE: Serum anion gap: Its uses and limitations in clinical medicine. Clin J Am Soc Nephrol 2: 162–174, 2007
https://doi.org/10.2215/CJN.03020906
19. Figge J, Jabor A, Kazda A, Fencl V: Anion gap and hypoalbuminemia. Crit Care Med 26: 1807–1810, 1998
https://doi.org/10.1097/00003246-199811000-00019
20. Shah SN, Abramowitz M, Hostetter TH, Melamed ML: Serum bicarbonate levels and the progression of kidney disease: A cohort study. Am J Kidney Dis 54: 270–277, 2009
https://doi.org/10.1053/j.ajkd.2009.02.014
21. Abramowitz MK, Hostetter TH, Melamed ML: Lower serum bicarbonate and a higher anion gap are associated with lower cardiorespiratory fitness in young adults. Kidney Int 81: 1033–1042, 2012
https://doi.org/10.1038/ki.2011.479
22. Taylor EN, Forman JP, Farwell WR: Serum anion gap and blood pressure in the national health and nutrition examination survey. Hypertension 50: 320–324, 2007
https://doi.org/10.1161/HYPERTENSIONAHA.107.092643
23. Chen YD, Varasteh BB, Reaven GM: Plasma lactate concentration in obesity and type 2 diabetes. Diabete Metab 19: 348–354, 1993
24. Dietch ZC, Guidry CA, Davies SW, Sawyer RG: Hypoalbuminemia is disproportionately associated with adverse outcomes in obese elective surgical patients. Surg Obes Relat Dis 11: 912–918, 2015
https://doi.org/10.1016/j.soard.2014.10.010
25. Goldenstein L, Driver TH, Fried LF, Rifkin DE, Patel KV, Yenchek RH, Harris TB, Kritchevsky SB, Newman AB, Sarnak MJ, Shlipak MG, Ix JH; Health ABC Study Investigators: Serum bicarbonate concentrations and kidney disease progression in community-living elders: The Health, Aging, and Body Composition (Health ABC) Study. Am J Kidney Dis 64: 542–549, 2014
https://doi.org/10.1053/j.ajkd.2014.05.009
26. Amodu A, Abramowitz MK: Dietary acid, age, and serum bicarbonate levels among adults in the United States. Clin J Am Soc Nephrol 8: 2034–2042, 2013
https://doi.org/10.2215/CJN.03600413
27. Kim JA, Wei Y, Sowers JR: Role of mitochondrial dysfunction in insulin resistance. Circ Res 102: 401–414, 2008
https://doi.org/10.1161/CIRCRESAHA.107.165472
28. Crawford SO, Ambrose MS, Hoogeveen RC, Brancati FL, Ballantyne CM, Young JH: Association of lactate with blood pressure before and after rapid weight loss. Am J Hypertens 21: 1337–1342, 2008
https://doi.org/10.1038/ajh.2008.282
29. Jansson PA, Larsson A, Smith U, Lönnroth P: Lactate release from the subcutaneous tissue in lean and obese men. J Clin Invest 93: 240–246, 1994
https://doi.org/10.1172/JCI116951
30. Reaven GM, Hollenbeck C, Jeng CY, Wu MS, Chen YD: Measurement of plasma glucose, free fatty acid, lactate, and insulin for 24 h in patients with NIDDM. Diabetes 37: 1020–1024, 1988
https://doi.org/10.2337/diab.37.8.1020
31. Juraschek SP, Selvin E, Miller ER, Brancati FL, Young JH: Plasma lactate and diabetes risk in 8045 participants of the atherosclerosis risk in communities study. Ann Epidemiol 23: 791–796.e4, 2013
https://doi.org/10.1016/j.annepidem.2013.09.005
32. Kreisberg RA: Lactate homeostasis and lactic acidosis. Ann Intern Med 92: 227–237, 1980
https://doi.org/10.7326/0003-4819-92-2-227
33. Brooks GA: Lactate as a fulcrum of metabolism. Redox Biol 35: 101454, 2020
https://doi.org/10.1016/j.redox.2020.101454
34. Robergs RA, Ghiasvand F, Parker D: Biochemistry of exercise-induced metabolic acidosis. Am J Physiol Regul Integr Comp Physiol 287: R502–R516, 2004
https://doi.org/10.1152/ajpregu.00114.2004
35. Ritov VB, Menshikova EV, He J, Ferrell RE, Goodpaster BH, Kelley DE: Deficiency of subsarcolemmal mitochondria in obesity and type 2 diabetes. Diabetes 54: 8–14, 2005
https://doi.org/10.2337/diabetes.54.1.8
36. Fischer B, Schöttl T, Schempp C, Fromme T, Hauner H, Klingenspor M, Skurk T: Inverse relationship between body mass index and mitochondrial oxidative phosphorylation capacity in human subcutaneous adipocytes. Am J Physiol Endocrinol Metab 309: E380–E387, 2015
https://doi.org/10.1152/ajpendo.00524.2014
37. Ritov VB, Menshikova EV, Azuma K, Wood R, Toledo FG, Goodpaster BH, Ruderman NB, Kelley DE: Deficiency of electron transport chain in human skeletal muscle mitochondria in type 2 diabetes mellitus and obesity. Am J Physiol Endocrinol Metab 298: E49–E58, 2010
https://doi.org/10.1152/ajpendo.00317.2009
38. Pérez-Carreras M, Del Hoyo P, Martín MA, Rubio JC, Martín A, Castellano G, Colina F, Arenas J, Solis-Herruzo JA: Defective hepatic mitochondrial respiratory chain in patients with nonalcoholic steatohepatitis. Hepatology 38: 999–1007, 2003
https://doi.org/10.1002/hep.1840380426
39. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D, Ilkayeva O, Wenner BR, Yancy WS Jr, Eisenson H, Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP: A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9: 311–326, 2009
https://doi.org/10.1016/j.cmet.2009.02.002
40. Serena C, Ceperuelo-Mallafré V, Keiran N, Queipo-Ortuño MI, Bernal R, Gomez-Huelgas R, Urpi-Sarda M, Sabater M, Pérez-Brocal V, Andrés-Lacueva C, Moya A, Tinahones FJ, Fernández-Real JM, Vendrell J, Fernández-Veledo S: Elevated circulating levels of succinate in human obesity are linked to specific gut microbiota. ISME J 12: 1642–1657, 2018
https://doi.org/10.1038/s41396-018-0068-2
41. Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA, Forslund K, Hildebrand F, Prifti E, Falony G, Le Chatelier E, Levenez F, Doré J, Mattila I, Plichta DR, Pöhö P, Hellgren LI, Arumugam M, Sunagawa S, Vieira-Silva S, Jørgensen T, Holm JB, Trošt K, Kristiansen K, Brix S, Raes J, Wang J, Hansen T, Bork P, Brunak S, Oresic M, Ehrlich SD, Pedersen O: Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535: 376–381, 2016
https://doi.org/10.1038/nature18646
42. Moore SC, Matthews CE, Sampson JN, Stolzenberg-Solomon RZ, Zheng W, Cai Q, Tan YT, Chow WH, Ji BT, Liu DK, Xiao Q, Boca SM, Leitzmann MF, Yang G, Xiang YB, Sinha R, Shu XO, Cross AJ: Human metabolic correlates of body mass index. Metabolomics 10: 259–269, 2014
https://doi.org/10.1007/s11306-013-0574-1
43. Widmer B, Gerhardt RE, Harrington JT, Cohen JJ: Serum electrolyte and acid base composition. The influence of graded degrees of chronic renal failure. Arch Intern Med 139: 1099–1102, 1979
https://doi.org/10.1001/archinte.1979.03630470021010
44. Dinh CH, Ng R, Grandinetti A, Joffe A, Chow DC: Correcting the anion gap for hypoalbuminaemia does not improve detection of hyperlactataemia. Emerg Med J 23: 627–629, 2006
https://doi.org/10.1136/emj.2005.031898
45. Wesson DE, Simoni J, Broglio K, Sheather S: Acid retention accompanies reduced GFR in humans and increases plasma levels of endothelin and aldosterone. Am J Physiol Renal Physiol 300: F830–F837, 2011
https://doi.org/10.1152/ajprenal.00587.2010
46. Gardner J, Tuttle K, Raphael KL: Influence of medications containing acid salts on serum bicarbonate in CKD. Kidney260 1: 330–336, 2020
47. Mokhlesi B, Tulaimat A, Faibussowitsch I, Wang Y, Evans AT: Obesity hypoventilation syndrome: Prevalence and predictors in patients with obstructive sleep apnea. Sleep Breath 11: 117–124, 2007
https://doi.org/10.1007/s11325-006-0092-8
48. Gabrielsen AM, Lund MB, Kongerud J, Viken KE, Røislien J, Hjelmesæth J: The relationship between anthropometric measures, blood gases, and lung function in morbidly obese white subjects. Obes Surg 21: 485–491, 2011
https://doi.org/10.1007/s11695-010-0306-9
49. Bobulescu IA, Park SK, Xu LHR, Blanco F, Poindexter J, Adams-Huet B, Davidson TL, Sakhaee K, Maalouf NM, Moe OW: Net acid excretion and urinary organic anions in idiopathic uric acid nephrolithiasis. Clin J Am Soc Nephrol 14: 411–420, 2019
https://doi.org/10.2215/CJN.10420818
50. Ward ZJ, Bleich SN, Cradock AL, Barrett JL, Giles CM, Flax C, Long MW, Gortmaker SL: Projected U.S. state-level prevalence of adult obesity and severe obesity. N Engl J Med 381: 2440–2450, 2019
https://doi.org/10.1056/NEJMsa1909301
