Associations between Dietary Acid Load and Biomarkers of Inflammation and Hyperglycemia in Breast Cancer Survivors
Abstract
:1. Introduction
2. Materials and Methods
2.1. Population
2.2. Dietary Assessment
2.3. Assessment of Dietary Acid Load
2.4. Measurement of CRP
2.5. Measurement of HbA1c
2.6. Other Assessments
2.7. Statistical Analyses
3. Results
4. Discussion
Strengths and Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- American Cancer Society. Cancer Facts and Figures 2019; American Cancer Society: Atlanta, Georgia, 2019. [Google Scholar]
- Fu, M.R.; Axelrod, D.; Guth, A.A.; Cleland, C.M.; Ryan, C.E.; Weaver, K.R.; Qiu, J.M.; Kleinman, R.; Scagliola, J.; Palamar, J.J.; et al. Comorbidities and Quality of Life among Breast Cancer Survivors: A Prospective Study. J. Pers. Med. 2015, 5, 229–242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Edgington, A. Looking Beyond Recurrence: Comorbidities in Cancer Survivors. Clin. J. Oncol. Nurs. 2011, 15, E3–E12. [Google Scholar] [CrossRef] [PubMed]
- Lipscombe, L.L.; Chan, W.W.; Yun, L.; Austin, P.C.; Anderson, G.M.; Rochon, P.A. Incidence of diabetes among postmenopausal breast cancer survivors. Diabetologia 2013, 56, 476–483. [Google Scholar] [CrossRef] [PubMed]
- Koenig, W.; Sund, M.; Frohlich, M.; Fischer, H.G.; Lowel, H.; Doring, A.; Hutchinson, W.L.; Pepys, M.B. C-reactive protein, a sensitive marker of inflammation, predicts future risk of coronary heart disease in initially healthy middle-aged men—Results from the MONICA (Monitoring Trends and Determinants in Cardiovascular Disease) Augsburg Cohort Study, 1984 to 1992. Circulation 1999, 99, 237–242. [Google Scholar] [PubMed]
- Ridker, P.M.; Hennekens, C.H.; Buring, J.E.; Rifai, N. C-Reactive Protein and Other Markers of Inflammation in the Prediction of Cardiovascular Disease in Women. N. Engl. J. Med. 2000, 342, 836–843. [Google Scholar] [CrossRef] [PubMed]
- Fujiwara, Y.; Haruki, K.; Shiba, H.; Hamura, R.; Horiuchi, T.; Shirai, Y.; Furukawa, K.; Gocho, T.; Yanaga, K. C-Reactive Protein-based Prognostic Measures Are Superior at Predicting Survival Compared with Peripheral Blood Cell Count-based Ones in Patients After Curative Resection for Pancreatic Cancer. Anticancer Res. 2018, 38, 6491–6499. [Google Scholar] [CrossRef] [PubMed]
- Chan, D.S.M.; Bandera, E.V.; Greenwood, D.C.; Norat, T. Circulating C-Reactive Protein and Breast Cancer Risk—Systematic Literature Review and Meta-analysis of Prospective Cohort Studies. Cancer Epidemiol. Biomark. Prev. 2015, 24, 1439. [Google Scholar] [CrossRef]
- Sherwani, S.I.; Khan, H.A.; Ekhzaimy, A.; Masood, A.; Sakharkar, M.K. Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomark. Insights 2016, 11, 95–104. [Google Scholar] [CrossRef]
- Erickson, K.; Patterson, R.E.; Flatt, S.W.; Natarajan, L.; Parker, B.A.; Heath, D.D.; Laughlin, G.A.; Saquib, N.; Rock, C.L.; Pierce, J.P. Clinically Defined Type 2 Diabetes Mellitus and Prognosis in Early-Stage Breast Cancer. J. Clin. Oncol. 2011, 29, 54–60. [Google Scholar] [CrossRef]
- Cordain, L.; Eaton, S.B.; Sebastian, A.; Mann, N.; Lindeberg, S.; Watkins, B.A.; O’Keefe, J.H.; Brand-Miller, J. Origins and evolution of the Western diet: Health implications for the 21st century. Am. J. Clin. Nutr. 2005, 81, 341–354. [Google Scholar] [CrossRef]
- Remer, T.; Manz, F. Estimation of the renal net acid excretion by adults consuming diets containing variable amounts of protein. Am. J. Clin. Nutr. 1994, 59, 1356–1361. [Google Scholar] [CrossRef] [PubMed]
- Sebastian, A.; Frassetto, L.A.; Sellmeyer, D.E.; Merriam, R.L.; Morris, R.C., Jr. Estimation of the net acid load of the diet of ancestral preagricultural Homo sapiens and their hominid ancestors. Am. J. Clin. Nutr. 2002, 76, 1308–1316. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ko, B.J.; Chang, Y.; Ryu, S.; Kim, E.M.; Lee, M.Y.; Hyun, Y.Y.; Lee, K.B. Dietary acid load and chronic kidney disease in elderly adults: Protein and potassium intake. PLoS ONE 2017, 12, e0185069. [Google Scholar] [CrossRef] [PubMed]
- Rebholz, C.M.; Coresh, J.; Grams, M.E.; Steffen, L.M.; Anderson, C.A.; Appel, L.J.; Crews, D.C. Dietary Acid Load and Incident Chronic Kidney Disease: Results from the ARIC Study. Am. J. Nephrol. 2015, 42, 427–435. [Google Scholar] [CrossRef] [PubMed]
- Banerjee, T.; Crews, D.C.; Wesson, D.E.; Tilea, A.; Saran, R.; Rios Burrows, N.; Williams, D.E.; Powe, N.R. Dietary acid load and chronic kidney disease among adults in the United States. BMC Nephrol. 2014, 15, 137. [Google Scholar] [CrossRef] [PubMed]
- Fagherazzi, G.; Vilier, A.; Bonnet, F.; Lajous, M.; Balkau, B.; Boutron-Rualt, M.C.; Clavel-Chapelon, F. Dietary acid load and risk of type 2 diabetes: The E3N-EPIC cohort study. Diabetologia 2014, 57, 313–320. [Google Scholar] [CrossRef] [PubMed]
- Pedoto, A.; Caruso, J.E.; Nandi, J.; Oler, A.; Hoffmann, S.P.; Tassiopoulos, A.K.; McGraw, D.J.; Camporesi, E.M.; Hakim, T.S. Acidosis stimulates nitric oxide production and lung damage in rats. Am. J. Respir. Crit. Care Med. 1999, 159, 397–402. [Google Scholar] [CrossRef]
- Pedoto, A.; Nandi, J.; Oler, A.; Camporesi, E.M.; Hakim, T.S.; Levine, R.A. Role of nitric oxide in acidosis-induced intestinal injury in anesthetized rats. J. Lab. Clin. Med. 2001, 138, 270–276. [Google Scholar] [CrossRef]
- Kellum, J.A.; Song, M.; Almasri, E. Hyperchloremic acidosis increases circulating inflammatory molecules in experimental sepsis. Chest 2006, 130, 962–967. [Google Scholar] [CrossRef]
- Sia, P.; Plumb, T.J.; Fillaus, J.A. Type B lactic acidosis associated with multiple myeloma. Am. J. Kidney Dis. 2013, 62, 633–637. [Google Scholar] [CrossRef]
- Pierce, J.P.; Faerber, S.; Wright, F.A.; Rock, C.L.; Newman, V.; Flatt, S.W.; Kealey, S.; Jones, V.E.; Caan, B.J.; Gold, E.B.; et al. A randomized trial of the effect of a plant-based dietary pattern on additional breast cancer events and survival: The Women’s Healthy Eating and Living (WHEL) Study. Control. Clin. Trials 2002, 23, 728–756. [Google Scholar] [CrossRef]
- Remer, T.; Manz, F. Potential Renal Acid Load of Foods and its Influence on Urine pH. J. Acad. Nutr. Diet. 1995, 95, 791–797. [Google Scholar] [CrossRef]
- Engberink, M.F.; Bakker, S.J.; Brink, E.J.; van Baak, M.A.; van Rooij, F.J.; Hofman, A.; Witteman, J.C.; Geleijnse, J.M. Dietary acid load and risk of hypertension: The Rotterdam Study. Am. J. Clin. Nutr. 2012, 95, 1438–1444. [Google Scholar] [CrossRef] [PubMed]
- Frassetto, L.A.; Todd, K.M.; Morris, R.C., Jr.; Sebastian, A. Estimation of net endogenous noncarbonic acid production in humans from diet potassium and protein contents. Am. J. Clin. Nutr. 1998, 68, 576–583. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Remer, T.; Dimitriou, T.; Manz, F. Dietary potential renal acid load and renal net acid excretion in healthy, free-living children and adolescents. Am. J. Clin. Nutr. 2003, 77, 1255–1260. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kiefte-de Jong, J.C.; Li, Y.; Chen, M.; Curhan, G.C.; Mattei, J.; Malik, V.S.; Forman, J.P.; Franco, O.H.; Hu, F.B. Diet-dependent acid load and type 2 diabetes: Pooled results from three prospective cohort studies. Diabetologia 2017, 60, 270–279. [Google Scholar] [CrossRef]
- Akter, S.; Eguchi, M.; Kuwahara, K.; Kochi, T.; Ito, R.; Kurotani, K.; Tsuruoka, H.; Nanri, A.; Kabe, I.; Mizoue, T. High dietary acid load is associated with insulin resistance: The Furukawa Nutrition and Health Study. Clin. Nutr. 2016, 35, 453–459. [Google Scholar] [CrossRef]
- Giugliano, D.; Ceriello, A.; Esposito, K. The Effects of Diet on Inflammation. J. Am. Coll. Cardiol. 2006, 48, 677. [Google Scholar] [CrossRef]
- Grivennikov, S.I.; Greten, F.R.; Karin, M. Immunity, Inflammation, and Cancer. Cell 2010, 140, 883–899. [Google Scholar] [CrossRef] [Green Version]
- Robey, I.F. Examining the relationship between diet-induced acidosis and cancer. Nutr. Metab. 2012, 9, 72. [Google Scholar] [CrossRef]
- Pierce, B.L.; Ballard-Barbash, R.; Bernstein, L.; Baumgartner, R.N.; Neuhouser, M.L.; Wener, M.H.; Baumgartner, K.B.; Gilliland, F.D.; Sorensen, B.E.; McTiernan, A.; et al. Elevated Biomarkers of Inflammation Are Associated With Reduced Survival Among Breast Cancer Patients. J. Clin. Oncol. 2009, 27, 3437–3444. [Google Scholar] [CrossRef] [PubMed]
- Allin, K.H.; Nordestgaard, B.G.; Flyger, H.; Bojesen, S.E. Elevated pre-treatment levels of plasma C-reactive protein are associated with poor prognosis after breast cancer: A cohort study. Breast Cancer Res. BCR 2011, 13, R55. [Google Scholar] [CrossRef] [PubMed]
- Pradhan, A.D.; Manson, J.E.; Rifai, N.; Buring, J.E.; Ridker, P.M. C-Reactive Protein, Interleukin 6, and Risk of Developing Type 2 Diabetes Mellitus. JAMA 2001, 286, 327–334. [Google Scholar] [CrossRef] [PubMed]
- McEvoy, J.W.; Nasir, K.; DeFilippis, A.P.; Lima, J.A.C.; Bluemke, D.A.; Hundley, W.G.; Barr, R.G.; Budoff, M.J.; Szklo, M.; Navas-Acien, A.; et al. Relationship of cigarette smoking with inflammation and subclinical vascular disease: The Multi-Ethnic Study of Atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 2015, 35, 1002–1010. [Google Scholar] [CrossRef] [PubMed]
- Attard, R.; Dingli, P.; Doggen, C.J.M.; Cassar, K.; Farrugia, R.; Wettinger, S.B. The impact of passive and active smoking on inflammation, lipid profile and the risk of myocardial infarction. Open Heart 2017, 4, e000620. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ghio, A.J.; Hilborn, E.D.; Stonehuerner, J.G.; Dailey, L.A.; Carter, J.D.; Richards, J.H.; Crissman, K.M.; Foronjy, R.F.; Uyeminami, D.L.; Pinkerton, K.E. Particulate Matter in Cigarette Smoke Alters Iron Homeostasis to Produce a Biological Effect. Am. J. Respir. Crit. Care Med. 2008, 178, 1130–1138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yacoub, R.; Habib, H.; Lahdo, A.; Al Ali, R.; Varjabedian, L.; Atalla, G.; Kassis Akl, N.; Aldakheel, S.; Alahdab, S.; Albitar, S. Association between smoking and chronic kidney disease: A case control study. BMC Public Health 2010, 10, 731. [Google Scholar] [CrossRef]
- Tantisuwat, A.; Thaveeratitham, P. Effects of smoking on chest expansion, lung function, and respiratory muscle strength of youths. J. Phys. Ther. Sci. 2014, 26, 167–170. [Google Scholar] [CrossRef]
- Law, M.R.; Hackshaw, A.K. A meta-analysis of cigarette smoking, bone mineral density and risk of hip fracture: Recognition of a major effect. BMJ 1997, 315, 841–846. [Google Scholar] [CrossRef]
- Coleman, R.E. Clinical Features of Metastatic Bone Disease and Risk of Skeletal Morbidity. Clin. Cancer Res. 2006, 12, 6243s–6249s. [Google Scholar] [CrossRef] [Green Version]
- USDA. Available online: https://ndb.nal.usda.gov/ndb/nutrients/report/nutrientsfrm?max=25&offset=0&totCount=0&nutrient1=304&nutrient2=&subset=0&sort=c&measureby=g (accessed on 24 July 2019).
- Wu, S.H.; Shu, X.O.; Chow, W.H.; Xiang, Y.B.; Zhang, X.; Li, H.L.; Cai, Q.; Ji, B.T.; Cai, H.; Rothman, N.; et al. Soy food intake and circulating levels of inflammatory markers in Chinese women. J. Acad. Nutr. Diet. 2012, 112, 996–1004. [Google Scholar] [CrossRef] [PubMed]
- Samraj, A.N.; Pearce, O.M.; Läubli, H.; Crittenden, A.N.; Bergfeld, A.K.; Banda, K.; Gregg, C.J.; Bingman, A.E.; Secrest, P.; Diaz, S.L.; et al. A red meat-derived glycan promotes inflammation and cancer progression. Proc. Natl. Acad. Sci. USA 2015, 112, 542–547. [Google Scholar] [CrossRef] [PubMed]
- Jiang, Y.; Wu, S.H.; Shu, X.O.; Xiang, Y.B.; Ji, B.T.; Milne, G.L.; Cai, Q.; Zhang, X.; Gao, Y.T.; Zheng, W.; et al. Cruciferous vegetable intake is inversely correlated with circulating levels of proinflammatory markers in women. J. Acad. Nutr. Diet. 2014, 114, 700–708. [Google Scholar] [CrossRef] [PubMed]
- Fenwick, G.R.; Heaney, R.K.; Mullin, W.J. Glucosinolates and their breakdown products in food and food plants. Crit. Rev. Food Sci. Nutr. 1983, 18, 123–201. [Google Scholar] [CrossRef] [PubMed]
- Scicchitano, P.; Cameli, M.; Maiello, M.; Modesti, P.A.; Muiesan, M.L.; Novo, S.; Palmiero, P.; Saba, P.S.; Pedrinelli, R.; Ciccone, M.M.; et al. Nutraceuticals and dyslipidaemia: Beyond the common therapeutics. J. Funct. Foods 2014, 6, 11–32. [Google Scholar] [CrossRef]
Characteristics | All Participants |
---|---|
Age at diagnosis, Mean (SD), Year | 50.7 (8.9) |
Ethnicity, N (%) | 2551 (85.7) |
White | |
African American | 105 (3.5) |
Hispanic | 159 (5.3) |
BMI, N (%) | 1274 (42.8) |
Normal | |
Overweight | 928 (31.2) |
Obese | 775 (26.0) |
Smoking Status, N (%) | 132 (4.4) |
Current | |
Former | 1230 (41.3) |
Never | 1585 (53.2) |
Menopause Status, N (%) | 332 (11.2) |
Premenopausal | |
Postmenopausal | 2370 (79.6) |
Perimenopausal | 270 (9.1) |
Stage, N (%) | 1153 (38.7) |
I | |
II | 1674 (56.2) |
IIIA | 150 (5.0) |
Hormone receptor, N (%) ER+/PR+ | 1908 (63.1) |
ER-/PR+ | 360 (12.1) |
ER+/PR- | 128 (4.3) |
ER-/PR- | 607 (20.4) |
Radiation, N (%) | 1134 (38.1) |
No | |
Yes | 1840 (61.8) |
Chemotherapy, N (%) | 903 (30.3) |
No | |
Yes | 2073 (69.6) |
Anti-estrogen therapy, N (%) | |
Tamoxifen | 2009 (66.0) |
Other anti-estrogens | 72 (2.4) |
None or unknown | 961 (31.6) |
Other medications, N (%) | |
Cardiovascular medicine | 383 (12.6) |
Blood sugar medicine or | 45 (1.5) |
corticosteroids | |
Gastrointestinal medicine | 206(6.8) |
None of the above | 2408 (79.0) |
METS minutes/week, N (%) | 1149 (38.6) |
0–600 | |
600–1200 | 702 (23.6) |
>1200 | 807 (27.1) |
PRAL | ||||
---|---|---|---|---|
1 | 2 | 3 | 4 | |
Age at diagnosis, mean (SD) | 52.6 (8.3) | 51.7 (8.8) | 50.5 (8.8) | 48.5 (9.0) |
Ethnicity, N (%) | ||||
White | 682 (89.7) | 677 (89.3) | 635 (84.6) | 587 (78.6) |
African American | 7 (0.9) | 16 (2.1) | 20 (2.7) | 65 (8.7) |
Hispanic | 26 (3.4) | 35 (4.6) | 45 (6.0) | 55 (7.3) |
Other/Mixed Race | 45 (5.9) | 30 (4.0) | 51 (6.8) | 40 (5.4) |
BMI, N (%) | 429 (56.4) | 355 (46.8) | 275 (36.6) | 243 (32.5) |
Normal | ||||
Overweight | 223 (29.3) | 230 (30.3) | 268 (35.7) | 211 (28.2) |
Obese | 108 (14.2) | 173 (22.8) | 208 (27.4) | 293 (39.2) |
Smoking Status, N (%) | 24 (3.2) | 34 (4.5) | 37 (5.0) | 40 (5.4) |
Current | ||||
Former | 324 (43.1) | 306 (40.6) | 314 (42.3) | 303 (41.0) |
Never | 403 (53.7) | 413 (54.8) | 392 (52.8) | 396 (53.6) |
METS minutes/week, N (%) | 230 (32.6) | 286 (41.6) | 313 (47.2) | 328 (51.7) |
0–600 | ||||
600–1200 | 206 (29.2) | 184 (26.8) | 185 (27.9) | 139 (21.9) |
>1200 | 269 (38.2) | 217 (31.6) | 165 (24.8) | 168 (26.5) |
Menopause Status, N (%) | 59 (7.8) | 74 (9.8) | 89 (11.9) | 116 (15.5) |
Premenopausal | ||||
Postmenopausal | 640 (84.3) | 609 (80.7) | 599 (79.8) | 546 (73.2) |
Perimenopausal | 60 (7.9) | 72 (9.5) | 63 (8.4) | 84 (11.3) |
Stage, N (%) | 298 (39.2) | 283 (37.3) | 289 (38.5) | 295 (39.5) |
I | ||||
II | 418 (55.0) | 448 (59.1) | 427 (56.9) | 406 (54.4) |
IIIA | 44 (5.8) | 27 (3.6) | 35 (4.7) | 46 (6.2) |
Hormone Receptor Status, N (%) | 488 (64.2) | 489 (64.5) | 480 (63.9) | 448 (60.0) |
ER+/PR+ | ||||
ER-/PR+ | 115 (15.1) | 92 (12.1) | 72 (9.6) | 86 (11.5) |
ER+/PR- | 30 (3.9) | 34 (4.5) | 30 (4.0) | 34 (4.6) |
ER-/PR- | 127 (16.7) | 143 (18.9) | 169 (22.5) | 179 (24.0) |
Radiation, N (%) | 275 (36.2) | 292 (38.5) | 305 (40.6) | 276 (37.0) |
No | ||||
Yes | 485 (63.8) | 466 (61.5) | 444 (59.1) | 470 (62.9) |
Chemotherapy, N (%) | 261 (34.3) | 237 (31.3) | 212 (28.2) | 199 (26.6) |
No | ||||
Yes | 499 (65.7) | 521 (68.7) | 538 (71.6) | 548 (73.4) |
PRAL | ||||||
Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | |||
Median | −21.046 | −8.28 | 0.09 | 11.02 | ||
Beta (p-value) | Beta (p-value) | Beta (p-value) | P for trend | |||
CRP | Age-adjusted | Ref | 0.18 (<0.0001) | 0.54 (<0.0001) | 0.84 (<0.0001) | <0.0001 |
Multivariable-adjusted | Ref | 0.09 (0.001) | 0.22 (0.0001) | 0.33 (<0.0001) | <0.0001 | |
HbA1c | Age-adjusted | Ref | 0.05 (0.002) | 0.14 (<0.0001) | 0.21 (<0.0001) | <0.0001 |
Multivariable-adjusted | Ref | 0.04 (0.03) | 0.07 (0.04) | 0.09 (0.01) | 0.01 | |
NEAP | ||||||
Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | |||
Median | 23.01 | 33.13 | 41.74 | 54.82 | ||
Beta (p-value) | Beta (p-value) | Beta (p-value) | P for trend | |||
CRP | Age-adjusted | Ref | 0.16 (<0.0001) | 0.56 (<0.0001) | 0.83 (<0.0001) | <0.0001 |
Multivariable-adjusted | Ref | 0.08 (0.0072) | 0.23 (<0.0001) | 0.31 (<0.001) | <0.0001 | |
HbA1c | Age-adjusted | Ref | 0.041 (0.01) | 0.14 (<0.0001) | 0.19 (<0.0001) | <0.0001 |
Multivariable-adjusted | Ref | 0.03 (0.13) | 0.06 (0.07) | 0.06 (0.08) | 0.10 |
CRP | PRAL | |||||
Q1 | Q2 | Q3 | Q4 | |||
n | Ref | Beta (p-value) | Beta (p-value) | Beta (p-value) | P for Interaction | |
Pack-Years | 0.14 | |||||
0 | 1643 | 0.11 (0.003) | 0.23 (0.004) | 0.22 (0.007) | ||
>0 | 1256 | 0.04 (0.31) | 0.23 (0.01) | 0.44 (<0.0001) | ||
CRP | NEAP | |||||
Q1 | Q2 | Q3 | Q4 | |||
n | Ref | Beta (p-value) | Beta (p-value) | Beta (p-value) | P for Interaction | |
Pack-Years | 0.06 | |||||
0 | 1643 | 0.08 (0.05) | 0.26 (0.001) | 0.26 (0.002) | ||
>0 | 1256 | 0.09 (0.04) | 0.22 (0.01) | 0.40 (< 0.0001) |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wu, T.; Seaver, P.; Lemus, H.; Hollenbach, K.; Wang, E.; Pierce, J.P. Associations between Dietary Acid Load and Biomarkers of Inflammation and Hyperglycemia in Breast Cancer Survivors. Nutrients 2019, 11, 1913. https://doi.org/10.3390/nu11081913
Wu T, Seaver P, Lemus H, Hollenbach K, Wang E, Pierce JP. Associations between Dietary Acid Load and Biomarkers of Inflammation and Hyperglycemia in Breast Cancer Survivors. Nutrients. 2019; 11(8):1913. https://doi.org/10.3390/nu11081913
Chicago/Turabian StyleWu, Tianying, Phoebe Seaver, Hector Lemus, Kathryn Hollenbach, Emily Wang, and John P. Pierce. 2019. "Associations between Dietary Acid Load and Biomarkers of Inflammation and Hyperglycemia in Breast Cancer Survivors" Nutrients 11, no. 8: 1913. https://doi.org/10.3390/nu11081913
APA StyleWu, T., Seaver, P., Lemus, H., Hollenbach, K., Wang, E., & Pierce, J. P. (2019). Associations between Dietary Acid Load and Biomarkers of Inflammation and Hyperglycemia in Breast Cancer Survivors. Nutrients, 11(8), 1913. https://doi.org/10.3390/nu11081913