Increased Acid-Producing Diet and Past Smoking Intensity Are Associated with Worse Prognoses among Breast Cancer Survivors: A Prospective Cohort Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Population
2.2. Dietary Assessment
2.3. Smoking Assessment
2.4. Assessment of Study Outcome
2.5. Other Assessments
2.6. Statistical Analyses
3. Results
3.1. Baseline Characteristics by Disease Outcomes in the Whole Cohort
3.2. Baseline Characteristics by Dietary Acid Load in the Whole Cohort
3.3. Dietary Acid Load, Past Smoking Intensity, and Risk of Total Mortality for Breast Cancer-Specific Mortality and Breast Cancer Recurrence
3.4. Joint Impact of Dietary Acid Load and Past Smoking Intensity on Breast Cancer Prognosis
3.5. Stratified Associations of Dietary Acid Load with Disease Outcomes by Past Smoking Intensity
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Henderson, T.O.; Ness, K.K.; Cohen, H.J. Accelerated aging among cancer survivors: From pediatrics to geriatrics. Am. Soc. Clin. Oncol. Educ. Book 2014, 34, e423–e430. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chang, L.; Weiner, L.S.; Hartman, S.J.; Horvath, S.; Jeste, D.; Mischel, P.S.; Kado, D.M. Breast cancer treatment and its effects on aging. J. Geriatr. Oncol. 2019, 10, 346–355. [Google Scholar] [CrossRef] [PubMed]
- Cupit-Link, M.C.; Kirkland, J.L.; Ness, K.K.; Armstrong, G.T.; Tchkonia, T.; LeBrasseur, N.K.; Armenian, S.H.; Ruddy, K.J.; Hashmi, S.K. Biology of premature ageing in survivors of cancer. ESMO Open 2017, 2, e000250. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cancer Facts and Figures. American Cancer Society, 2019.
- Rico-Campa, A.; A Martínez-González, M.; Alvarez-Alvarez, I.; Mendonça, R.D.D.; De La Fuente-Arrillaga, C.; Gómez-Donoso, C.; Bes-Rastrollo, M. Association between consumption of ultra-processed foods and all cause mortality: SUN prospective cohort study. BMJ 2019, 365, l1949. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Veronese, N.; Cisternino, A.M.; Shivappa, N.; Hebert, J.R.; Notarnicola, M.; Reddavide, R.; Inguaggiato, R.; Guerra, V.; Logroscino, A.; Rotolo, O.; et al. Dietary inflammatory index and mortality: A cohort longitudinal study in a Mediterranean area. J. Hum. Nutr. Diet. 2020, 33, 138–146. [Google Scholar] [CrossRef]
- Hamm, L.L.; Nakhoul, N.; Hering-Smith, K.S. Acid-Base Homeostasis. Clin. J. Am. Soc. Nephrol. 2015, 10, 2232–2242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Polak, A.; Haynie, G.D.; Hays, R.M.; Schwartz, W.B. Effects of chronic hypercapnia on electrolyte and acid-base equilibrium. I. Adaptation. J. Clin. Investig. 1961, 40, 1223–1237. [Google Scholar] [CrossRef]
- Madias, N.E.; Adrogue, H.J. Cross-talk between two organs: How the kidney responds to disruption of acid-base balance by the lung. Nephron. Physiol. 2003, 93, 61–66. [Google Scholar] [CrossRef]
- Akter, S.; Nanri, A.; Mizoue, T.; Noda, M.; Sawada, N.; Sasazuki, S.; Tsugane, S. Dietary acid load and mortality among Japanese men and women: The Japan Public Health Center-based Prospective Study. Am. J. Clin. Nutr. 2017, 106, 146–154. [Google Scholar] [CrossRef] [Green Version]
- Abbasalizad Farhangi, M.; Vajdi, M.; Najafi, M. Dietary acid load significantly predicts 10-years survival in patients underwent coronary artery bypass grafting (CABG) surgery. PLoS ONE 2019, 14, e0223830. [Google Scholar] [CrossRef] [Green Version]
- Shirali, A. Electrolyte and Acid–Base Disorders in Malignancy. In Onco-Nephrology Curriculum; American Society of Nephrology: San Diego, CA, USA, 2016. [Google Scholar]
- Buehler, J.H.; Berns, A.S.; Webster, J.R.; Addington, W.W.; Cugell, D.W. Lactic acidosis from carboxyhemoglobinemia after smoke inhalation. Ann. Intern. Med. 1975, 82, 803–805. [Google Scholar] [CrossRef]
- Alguacil, J.; Kogevinas, M.; Silverman, D.T.; Malats, N.; Real, F.X.; García-Closas, M.; Tardon, A.; Rivas, M.; Torà, M.; García-Closas, R.; et al. Urinary pH, cigarette smoking and bladder cancer risk. Carcinogenesis 2011, 32, 843–847. [Google Scholar] [CrossRef] [PubMed]
- Wu, T.; Seaver, P.; Lemus, H.; Hollenbach, K.; Wang, S.E.; Pierce, J. Associations between Dietary Acid Load and Biomarkers of Inflammation and Hyperglycemia in Breast Cancer Survivors. Nutrients 2019, 11, 1913. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Saquib, N.; Stefanick, M.L.; Natarajan, L.; Pierce, J. Mortality risk in former smokers with breast cancer: Pack-years vs. smoking status. Int. J. Cancer 2013, 133, 2493–2497. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Munger, K.L.; Levin, L.I.; Massa, J.; Horst, R.; Orban, T.; Ascherio, A. Preclinical serum 25-hydroxyvitamin D levels and risk of type 1 diabetes in a cohort of US military personnel. Am. J. Epidemiol. 2013, 177, 411–419. [Google Scholar] [CrossRef] [PubMed]
- Lafourcade, A.; His, M.; Baglietto, L.; Boutron-Ruault, M.-C.; Dossus, L.; Rondeau, V. Factors associated with breast cancer recurrences or mortality and dynamic prediction of death using history of cancer recurrences: The French E3N cohort. BMC Cancer 2018, 18, 171. [Google Scholar] [CrossRef]
- Duan, W.; Li, S.; Meng, X.; Sun, Y.; Jia, C. Smoking and survival of breast cancer patients: A meta-analysis of cohort studies. Breast 2017, 33, 117–124. [Google Scholar] [CrossRef]
- Pierce, J.P.; Faerber, S.; A Wright, F.; Rock, C.L.; Newman, V.; Flatt, S.W.; Kealey, S.; E Jones, V.; 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]
- Pierce, J.P.; Natarajan, L.; Caan, B.J.; Parker, B.A.; Greenberg, E.R.; Flatt, S.W.; Rock, C.L.; Kealey, S.; Al-Delaimy, W.K.; Bardwell, W.A.; et al. Influence of a diet very high in vegetables, fruit, and fiber and low in fat on prognosis following treatment for breast cancer: The Women’s Healthy Eating and Living (WHEL) randomized trial. JAMA 2007, 298, 289–298. [Google Scholar] [CrossRef]
- Remer, T.; Manz, F. Potential renal acid load of foods and its influence on urine pH. J. Am. Diet Assoc. 1995, 95, 791–797. [Google Scholar] [CrossRef]
- Frassetto, L.A.; Todd, K.M.; Morris, R.C.; 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] [Green Version]
- Engberink, M.F.; Bakker, S.J.; Brink, E.J.; Van Baak, M.; 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] [Green Version]
- Frassetto, L.A.; Morris, R.C., Jr.; Sebastian, A. A practical approach to the balance between acid production and renal acid excretion in humans. J. Nephrol. 2006, 19 (Suppl. 9), S33–S40. [Google Scholar] [PubMed]
- Johnson-Kozlow, M.; Rock, C.L.; Gilpin, E.A.; Hollenbach, K.A.; Pierce, J. Validation of the WHI brief physical activity questionnaire among women diagnosed with breast cancer. Am. J. Health Behav. 2007, 31, 193–202. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hong, S.; Bardwell, W.A.; Natarajan, L.; Flatt, S.W.; Rock, C.L.; Newman, V.A.; Madlensky, L.; Mills, P.J.; Dimsdale, J.E.; Thomson, C.A.; et al. Correlates of physical activity level in breast cancer survivors participating in the Women’s Healthy Eating and Living (WHEL) Study. Breast Cancer Res. Treat 2007, 101, 225–232. [Google Scholar] [CrossRef]
- Xu, H.; Åkesson, A.; Orsini, N.; Håkansson, N.; Wolk, A.; Carrero, J.-J. Modest U-Shaped Association between Dietary Acid Load and Risk of All-Cause and Cardiovascular Mortality in Adults. J. Nutr. 2016, 146, 1580–1585. [Google Scholar] [CrossRef] [Green Version]
- Park, M.; Jung, S.J.; Yoon, S.; Yun, J.M.; Yoon, H.-J. Association between the markers of metabolic acid load and higher all-cause and cardiovascular mortality in a general population with preserved renal function. Hypertens Res. 2015, 38, 433–438. [Google Scholar] [CrossRef]
- Fagherazzi, G.; Vilier, A.; Bonnet, F.; Lajous, M.; Balkau, B.; Boutron-Ruault, 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] [Green Version]
- Zhang, L.; Curhan, G.C.; Forman, J.P. Diet-dependent net acid load and risk of incident hypertension in United States women. Hypertension 2009, 54, 751–755. [Google Scholar] [CrossRef]
- 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] [Green Version]
- Sellmeyer, D.E.; Stone, K.L.; Sebastian, A.; Cummings, S.R. A high ratio of dietary animal to vegetable protein increases the rate of bone loss and the risk of fracture in postmenopausal women. Study of Osteoporotic Fractures Research Group. Am. J. Clin. Nutr. 2001, 73, 118–122. [Google Scholar]
- Gaede, J.; Nielsen, T.; Madsen, M.L.; Toft, U.; Jorgensen, T.; Overvad, K.; Tjonneland, A.; Hansen, T.; Allin, K.H. Pedersen, O. Population-based studies of relationships between dietary acidity load, insulin resistance and incident diabetes in Danes. Nutr. J. 2018, 17, 91. [Google Scholar] [CrossRef] [PubMed]
- Rawshani, A.; Franzén, S.; Sattar, N.; Eliasson, B.; Svensson, A.-M.; Zethelius, B.; Miftaraj, M.; McGuire, D.K.; Rosengren, A.; Gudbjörnsdottir, S. Risk Factors, Mortality, and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N. Engl. J. Med. 2018, 379, 633–644. [Google Scholar] [CrossRef] [PubMed]
- Morri, M.; Ambrosi, E.; Chiari, P.; Magli, A.O.; Gazineo, D.; Alessandro, F.D.; Forni, C. One-year mortality after hip fracture surgery and prognostic factors: A prospective cohort study. Sci. Rep. 2019, 9, 18718. [Google Scholar] [CrossRef] [PubMed]
- Wen, C.P.; Cheng, T.Y.D.; Tsai, M.-K.; Chang, Y.C.; Chan, H.-T.; Tsai, S.P.; Chiang, P.H.; Hsu, C.-C.; Sung, P.K.; Hsu, Y.H.; et al. All-cause mortality attributable to chronic kidney disease: A prospective cohort study based on 462 293 adults in Taiwan. Lancet 2008, 371, 2173–2182. [Google Scholar] [CrossRef]
- Kato, Y.; Ozawa, S.; Miyamoto, C.; Maehata, Y.; Suzuki, A.; Maeda, T.; Baba, Y. Acidic extracellular microenvironment and cancer. Cancer Cell Int. 2013, 13, 89. [Google Scholar] [CrossRef] [Green Version]
- Boedtkjer, E.; Pedersen, S.F. The Acidic Tumor Microenvironment as a Driver of Cancer. Annu. Rev. Physiol. 2020, 82, 103–126. [Google Scholar]
- Calcinotto, A.; Filipazzi, P.; Grioni, M.; Iero, M.; De Milito, A.; Ricupito, A.; Cova, A.; Canese, R.; Jachetti, E.; Rossetti, M.; et al. Modulation of microenvironment acidity reverses anergy in human and murine tumor-infiltrating T lymphocytes. Cancer Res. 2012, 72, 2746–2756. [Google Scholar] [CrossRef] [Green Version]
- Huber, V.; Camisaschi, C.; Berzi, A.; Ferro, S.; Lugini, L.; Triulzi, T.; Tuccitto, A.; Tagliabue, E.; Castelli, C.; Rivoltini, L. Cancer acidity: An ultimate frontier of tumor immune escape and a novel target of immunomodulation. Semin. Cancer Biol. 2017, 43, 74–89. [Google Scholar] [CrossRef]
- Tredan, O.; Galmarini, C.M.; Patel, K.; Tannock, I.F. Drug resistance and the solid tumor microenvironment. J. Natl. Cancer Inst. 2007, 99, 1441–1454. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- 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]
- Daneshzad, E.; Keshavarz, S.-A.; Qorbani, M.; Larijani, B.; Bellissimo, N.; Azadbakht, L. Association of dietary acid load and plant-based diet index with sleep, stress, anxiety and depression in diabetic women. Br. J. Nutr. 2020, 123, 901–912. [Google Scholar] [CrossRef] [PubMed]
- Buhlmeier, J.; Harris, C.; Koletzko, S.; Lehmann, I.; Bauer, C.-P.; Schikowski, T.; Von Berg, A.; Berdel, D.; Heinrich, J.; Hebebrand, J.; et al. Dietary Acid Load and Mental Health Outcomes in Children and Adolescents: Results from the GINIplus and LISA Birth Cohort Studies. Nutrients 2018, 10, 582. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Marks, A.R. Calcium and the heart: A question of life and death. J. Clin. Investig. 2003, 111, 597–600. [Google Scholar] [CrossRef] [PubMed]
- Taveira, T.H.; Ouellette, D.; Gulum, A.; Choudhary, G.; Eaton, C.B.; Liu, S.; Wu, W.-C. Relation of Magnesium Intake with Cardiac Function and Heart Failure Hospitalizations in Black Adults: The Jackson Heart Study. Circ. Heart Fail. 2016, 9, e002698. [Google Scholar] [CrossRef] [Green Version]
- Yary, T.; Lehto, S.M.; Tolmunen, T.; Tuomainen, T.-P.; Kauhanen, J.; Voutilainen, S.; Ruusunen, A. Dietary magnesium intake and the incidence of depression: A 20-year follow-up study. J. Affect. Disord. 2016, 193, 94–98. [Google Scholar] [CrossRef]
- Tarleton, E.K.; Littenberg, B. Magnesium intake and depression in adults. J. Am. Board Fam. Med. 2015, 28, 249–256. [Google Scholar] [CrossRef] [Green Version]
- Frizel, D.; Coppen, A.; Marks, V. Plasma magnesium and calcium in depression. Br. J. Psychiatry 1969, 115, 1375–1377. [Google Scholar] [CrossRef]
- Pierce, J.P.; Patterson, R.E.; Senger, C.M.; Flatt, S.W.; Caan, B.J.; Natarajan, L.; Nechuta, S.J.; Poole, E.M.; Shu, X.-O.; Chen, W.Y. Lifetime cigarette smoking and breast cancer prognosis in the After Breast Cancer Pooling Project. J. Natl. Cancer Inst. 2014, 106, djt359. [Google Scholar] [CrossRef] [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.; Akl, N.K.; 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] [Green Version]
- 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] [Green Version]
- 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] [Green Version]
- Okada, Y.; Tsuzuki, Y.; Ueda, T.; Hozumi, H.; Sato, S.; Hokari, R.; Kurihara, C.; Watanabe, C.; Tomita, K.; Komoto, S.; et al. Trans fatty acids in diets act as a precipitating factor for gut inflammation? J. Gastroenterol. Hepatol. 2013, 28 (Suppl. 4), 29–32. [Google Scholar] [CrossRef] [PubMed]
- Fabricatore, A.N.; Ebbeling, C.B.; A Wadden, T.; Ludwig, D.S. Continuous glucose monitoring to assess the ecologic validity of dietary glycemic index and glycemic load. Am. J. Clin. Nutr. 2011, 94, 1519–1524. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- 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. Clinically defined type 2 diabetes mellitus and prognosis in early-stage breast cancer. J. Clin. Oncol. 2011, 29, 54–60. [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]
- Bonaccio, M.; Di Castelnuovo, A.; Pounis, G.; De Curtis, A.; Costanzo, S.; Persichillo, M.; Cerletti, C.; Donati, M.; De Gaetano, G.; Iacoviello, L. A score of low-grade inflammation and risk of mortality: Prospective findings from the Moli-sani study. Haematologica 2016, 101, 1434–1441. [Google Scholar] [CrossRef] [Green Version]
- Proctor, M.J.; McMillan, D.C.; Horgan, P.G.; Fletcher, C.D.; Talwar, D.; Morrison, D.S. Systemic inflammation predicts all-cause mortality: A glasgow inflammation outcome study. PLoS ONE 2015, 10, e0116206. [Google Scholar] [CrossRef] [Green Version]
- Rock, C.L.; Doyle, C.; Demark-Wahnefried, W.; Meyerhardt, J.; Courneya, K.S.; Schwartz, A.L.; Bandera, E.V.; Hamilton, K.K.; Grant, B.; McCullough, M.; et al. Nutrition and physical activity guidelines for cancer survivors. CA Cancer J. Clin. 2012, 62, 243–274. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Runowicz, C.D.; Leach, C.R.; Henry, N.L.; Henry, K.S.; Mackey, H.T.; Cowens-Alvarado, R.L.; Cannady, R.S.; Pratt-Chapman, M.L.; Edge, S.B.; Jacobs, L.A.; et al. American Cancer Society/American Society of Clinical Oncology Breast Cancer Survivorship Care Guideline. J. Clin. Oncol. 2016, 34, 611–635. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Total Mortality | Breast Cancer Specific Morality | Breast Cancer Recurrence | |||||||
---|---|---|---|---|---|---|---|---|---|
No (N = 2655) | Yes (N = 295) | p-Value | No (N = 2655) | Yes (N = 249) | p-Value | No (N = 2460) | Yes (N = 490) | p-Value | |
PRAL (mEq/day) a | −3.97 (−14.11 to 4.42) | −2.93 (−13.12 to 5.17) | 0.3 | −3.97 (−14.10 to 4.42) | −2.52 (−12.59 to 5.85) | 0.2 | −4.10 (−14.15 to 4.42) | −2.84 (−13.14 to 5.17) | 0.1 |
NEAL (mEq/day) | 39.78 (32.25 to 48.22) | 40.79 (33.12 to 48.89) | 0.3 | 39.80 (32.21 to 48.22) | 41.03 (33.50 to 48.68) | 0.3 | 39.65 (32.08 to 48.22) | 40.87 (33.36 to 48.68) | 0.2 |
Basic | |||||||||
Age at diagnosis (years) | 50.0 (45.0–57.0) | 51.0 (44.0–59.0) | 0.3 | 50.0 (45.0–57.0) | 50.0 (43.0–57.0) | 0.2 | 50.0 (45.0–57.0) | 49.0 (42.0–56.0) | 0.3 |
White (%) | 85.4 | 82.4 | 0.2 | 85.4 | 83.1 | 0.3 | 85.4 | 85.5 | 0.7 |
Body mass index | |||||||||
Normal weight (%) | 44.0 | 37.3 | 0.006 | 44.0 | 39.0 | 0.03 | 43.5 | 42.7 | 0.2 |
Overweight an obese (%) | 56.0 | 63.7 | 56.0 | 61.0 | 56.4 | 57.3 | |||
Education, at or above college (%) | 56.3 | 46.8 | 0.002 | 56.3 | 46.4 | 0.0003 | 56.3 | 50.4 | 0.04 |
Postmenopausal women (%) | 79.2 | 79.7 | 0.3 | 79.2 | 76.7 | 0.3 | 80.2 | 74.5 | 0.001 |
Smoking status | |||||||||
Past smoker (%) | 43.2 | 48.1 | 0.1 | 43.2 | 47.3 | 0.2 | 43.4 | 43.8 | 0.9 |
Never smoker (%) | 56.8 | 51.9 | 56.8 | 52.7 | 56.6 | 56.2 | |||
Pack-year status | |||||||||
Pack-years = 0 (%) | 56.3 | 50.8 | <0.0001 | 56.3 | 51.4 | <0.0001 | 55.7 | 55.7 | 0.11 |
Pack-years >0 to 15 (%) | 27.8 | 21.7 | 27.8 | 22.5 | 27.7 | 24.9 | |||
Pack-years >15 (%) | 14.5 | 23.4 | 14.5 | 21.3 | 15.1 | 16.5 | |||
Alcohol abstainer (%) | 31.2 | 35.9 | 0.1 | 31.2 | 35.9 | 0.1 | 31.3 | 33.5 | 0.5 |
Physical activity (MET/week) | 600 (180–1300) | 450 (105–930) | 0.001 | 600 (180–1300) | 435 (100–975) | 0.003 | 600 (180–1295) | 525 (120–1110) | 0.09 |
Intervention group (%) | 49.8 | 50.2 | 0.9 | 49.9 | 48.6 | 0.7 | 49.6 | 50.1 | 0.9 |
Chemotherapy (%) | 68.8 | 80.3 | 0.0002 | 68.8 | 86.8 | <0.0001 | 67.9 | 80.6 | <0.0001 |
Radiation (%) | 61.8 | 61.4 | 0.8 | 61.8 | 61.9 | 0.8 | 61.6 | 62.5 | 0.8 |
Hormone receptor status | |||||||||
ER+/PR+ (%) | 62.9 | 50.9 | 0.0002 | 62.9 | 47.8 | <0.0001 | 62.9 | 55.3 | 0.01 |
ER-/PR- (%) | 21.3 | 29.8 | 21.3 | 32.5 | 19.1 | 24.5 | |||
Cancer stage at diagnosis (%) | |||||||||
I | 40.4 | 20.0 | <0.0001 | 40.4 | 14.5 | <0.0001 | 41.9 | 20.2 | <0.0001 |
II | 55.5 | 67.1 | 55.5 | 71.1 | 54.1 | 69.6 | |||
IIIa | 4.2 | 12.9 | 4.2 | 14.5 | 4.0 | 10.2 | |||
Tamoxifen use (%) | 66.8 | 61.0 | 0.1 | 66.8 | 57.4 | 0.009 | 67.6 | 59.4 | 0.001 |
PRAL Score Quartiles (mEq/day) | |||||
---|---|---|---|---|---|
Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | p-Value | |
<−13.7 (n = 771) | −13.7 to <−3.7 (n = 769) | −3.7 to <4.7 (n = 771) | ≥4.7 (n = 770) | ||
NEAL (mEq/day) a | 27.4 (23.9–30.7) | 36.4 (33.7–38.5) | 43.7 (41.1–46.3) | 55.4 (50.9–61.3) | <0.001 |
Basic | |||||
Age at diagnosis (years) | 52.0 (47.0–58.0) | 51.0 (46.0–58.0) | 50.0 (45.0–57.0) | 48.0 (42.0–55.0) | <0.001 |
White (%) | 89.6 | 88.7 | 83.8 | 78.2 | <0.001 |
Body mass index | |||||
Normal weight (%) | 56.6 | 46.7 | 37.1 | 32.8 | <0.001 |
Overweight and obese (%) | 43.4 | 53.3 | 63.9 | 67.2 | |
Education, at or above college (%) | 64.8 | 57.4 | 52.7 | 46.3 | <0.001 |
Postmenopausal women (%) | 84.5 | 80.1 | 80.0 | 73.2 | 0.001 |
Smoking status | |||||
Past smoker (%) | 44.6 | 43.0 | 44.1 | 43.1 | 0.9 |
Never smoker (%) | 55.4 | 56.9 | 55.9 | 56.9 | |
Pack-year status | |||||
Pack-years = 0 (%) | 54.8 | 56.6 | 55.3 | 56.3 | 0.06 |
Pack-years > 0 to 15 (%) | 28.0 | 24.6 | 27.9 | 28.3 | |
Pack-years > 15 (%) | 15.8 | 17.7 | 14.3 | 13.6 | |
Alcohol abstainer (%) | 32.1 | 30.5 | 33.7 | 30.8 | 0.3 |
Physical activity (MET/week) | 825 (330–1500) | 630 (225–1335) | 480 (150–1080) | 405 (60–1080) | <0.001 |
Chemotherapy (%) | 63.6 | 61.4 | 59.5 | 62.5 | 0.3 |
Radiation (%) | 63.6 | 61.0 | 59.1 | 62.2 | 0.6 |
Hormone receptor status | |||||
ER+/PR+ (%) | 63.2 | 63.1 | 62.3 | 58.1 | 0.003 |
ER−/PR− (%) | 16.2 | 18.8 | 21.7 | 23.6 | |
Cancer stage at diagnosis (%) | |||||
I | 38.8 | 36.7 | 38.7 | 38.9 | 0.4 |
II | 55.4 | 59.6 | 56.7 | 55.0 | |
III a | 5.7 | 3.7 | 4.6 | 6.2 | |
Tamoxifen use (%) | 72.0 | 66.9 | 63.6 | 62.2 | 0.001 |
Total Mortality | Breast Cancer-Specific Mortality | Breast Cancer Recurrence | |||||
---|---|---|---|---|---|---|---|
Event | HR (95%CI) | Event | HR (95%CI) | Event | HR (95% CI) | ||
Dietary acid load | |||||||
PRAL(mEq/day) | Range | ||||||
Quartile 1 | <−19.50 | 40 | Ref | 34 | Ref | 61 | Ref |
Quartile 2 | −19.50 to <−6.94 | 77 | 1.17 (0.81–1.69) | 60 | 1.08 (0.73–1.54) | 133 | 0.98 (0.76–1.27) |
Quartile 3 | −6.94 to <3.22 | 89 | 1.41 (0.97–2.06) | 80 | 1.43 (0.96–2.13) | 147 | 1.07 (0.82–1.39) |
Quartile 4 | ≥3.22 | 89 | 1.30 (0.87–1.94) | 75 | 1.27 (0.83–1.94) | 149 | 1.09 (0.83–1.43) |
P for trend | 0.09 | 0.09 | 0.5 | ||||
NEAP(mEq/day) | Range | ||||||
Quartile 1 | <28.44 | 35 | Ref | 29 | Ref | 61 | Ref |
Quartile 2 | 28.44 to <37.25 | 82 | 1.27 (0.88–1.84) | 66 | 1.27 (0.87–1.87) | 127 | 1.06 (0.82–1.37) |
Quartile 3 | 37.25 to <46.90 | 86 | 1.50 (1.02–2.21) | 77 | 1.46 (0.96–2.21) | 152 | 1.01 (0.77–1.32) |
Quartile 4 | ≥46.90 | 92 | 1.54 (1.04–2.29) | 77 | 1.52 (1.01–2.32) | 150 | 1.15 (0.88–1.50) |
P for trend | 0.03 | 0.04 | 0.4 | ||||
Past smoking intensity | |||||||
Pack-year category | Range | ||||||
1 | 0 | 150 | Ref | 128 | Ref | 273 | Ref |
2 | 0–15 | 64 | 0.96 (0.71–1.28) | 56 | 1.02 (0.75–1.39) | 122 | 0.96 (0.77–1.17) |
3 | 15+ | 69 | 1.71 (1.28–2.31) | 53 | 1.68 (1.23–2.30) | 81 | 1.17 (0.91–1.51) |
P for trend | <0.0001 | 0.001 | 0.03 |
PRAL (mEq/day) | NEAP (mEq/day) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Tertile 1 | Tertile 2 | Tertile 3 | Tertile 1 | Tertile 2 | Tertile 3 | |||||||
<−15.04 | −15.04 to <−0.71 | ≥ −0.71 | <31.5 | 31.5 to <43.4 | ≥43.4 | |||||||
Total mortality | ||||||||||||
N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | |
Pack-years = 0 | 32 | Ref | 60 | 1.48 (0.98–2.24) | 58 | 1.16 (0.76–1.78) | 33 | Ref | 58 | 1.39 (0.92–2.08) | 59 | 1.18 (0.76–1.81) |
0< Pack-years ≤15 | 10 | 1.13 (0.69–1.85) | 24 | 1.01 (0.58–1.77) | 30 | 1.25 (0.74–2.10) | 10 | 1.05 (0.63–1.74) | 25 | 1.10 (0.65–1.86) | 29 | 1.22 (0.72–2.06) |
Pack-years > 15 | 16 | 1.26 (0.71–2.21) | 24 | 2.20 (1.33–3.66) | 29 | 2.86 (1.73–4.74) | 15 | 1.35 (0.78–2.35) | 23 | 1.67 (0.97–2.88) | 31 | 3.23 (1.99–5.26) |
P for trend | 0.004 | 0.0001 | ||||||||||
Breast cancer-specific mortality | ||||||||||||
N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | |
Pack-years = 0 | 25 | Ref | 50 | 1.39 (0.89–2.20) | 53 | 1.13 (0.71–1.79) | 26 | Ref | 49 | 1.20 (0.77–1.89) | 53 | 1.08 (0.68–1.72) |
0< Pack-years ≤15 | 9 | 1.17 (0.70–1.99) | 20 | 0.92 (0.50–1.70) | 27 | 1.36 (0.78–2.37) | 8 | 1.04 (0.62–1.76) | 22 | 0.99 (0.56–1.75) | 26 | 1.26 (0.72–2.21) |
Pack-years > 15 | 13 | 1.12 (0.61–2.06) | 19 | 2.08 (1.19–3.63) | 21 | 2.65 (1.54–4.57) | 11 | 1.19 (0.66–2.14) | 19 | 1.48 (0.82–2.68) | 23 | 2.82 (1.67–4.76) |
P for trend | 0.002 | 0.002 | ||||||||||
Breast cancer recurrence | ||||||||||||
N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | N | HR (95%CI) | |
Pack-years = 0 | 56 | Ref | 106 | 0.90 (0.68–1.23) | 111 | 0.90 (0.67–1.21) | 56 | Ref | 105 | 0.97 (0.72–1.30) | 112 | 0.94 (0.69–1.27) |
0< Pack-years ≤15 | 19 | 0.88 (0.62–1.25) | 48 | 0.88 (0.61–1.28) | 55 | 0.90 (0.62–1.29) | 20 | 0.94 (0.66-1.34) | 47 | 0.80 (0.54–1.17) | 55 | 1.01 (0.70–1.46) |
Pack-years > 15 | 18 | 0.79 (0.50–1.25) | 32 | 0.97 (0.68–1.52) | 31 | 1.69 (1.10–2.64) | 15 | 0.84 (0.53–1.34) | 34 | 1.03 (0.66–1.59) | 32 | 1.64 (1.09–2.46) |
P for trend | 0.1 | 0.1 |
Total Morality | Breast Cancer-Specific Morality | Breast Cancer Recurrence | ||||
---|---|---|---|---|---|---|
PRAL (mEq/day) | ||||||
Pack-Years = 0 | Range | Events | HR (95%CI) | HR (95%CI) | Events | HR (95%CI) |
Quartile 1 | <−19.50 | 21 | Ref | Ref | 38 | Ref |
Quartile 2 | −19.50 to <−6.94 | 41 | 1.07 (0.64–1.79) | 1.04 (0.58–1.85) | 75 | 0.95 (0.67–1.32) |
Quartile 3 | −6.94 to <3.22 | 45 | 1.35 (0.80–2.29) | 1.31 (0.71–2.43) | 73 | 0.94 (0.65–1.34) |
Quartile 4 | ≥3.22 | 43 | 1.10 (0.63–1.93) | 1.13 (0.60–2.10) | 87 | 0.98 (0.68–1.40) |
P for trend | 0.6 | 0.6 | 0.9 | |||
Pack-Years > 0 | Range | Events | HR (95%CI) | HR (95%CI) | Events | HR (95%CI) |
Quartile 1 | <−19.50 | 17 | Ref | Ref | 21 | Ref |
Quartile 2 | −19.50 to <−6.94 | 33 | 1.17 (0.69–1.99) | 1.03 (0.56–1.90) | 53 | 0.98 (0.64–1.50) |
Quartile 3 | −6.94 to <3.22 | 41 | 1.45 (0.84–2.49) | 1.54 (0.86–2.75) | 71 | 1.34 (0.89–2.03) |
Quartile 4 | ≥3.22 | 42 | 1.51 (0.84–2.69) | 1.54 (0.79–3.01) | 58 | 1.28 (0.83–1.99) |
P for trend | 0.1 | 0.6 | 0.1 | |||
P for interaction | 0.4 | 0.09 | 0.03 | |||
NEAP (mEq/day) | ||||||
Pack-Years = 0 | Range | Events | HR (95%CI) | HR (95%CI) | Events | HR (95%CI) |
Quartile 1 | <28.44 | 17 | Ref | Ref | 32 | Ref |
Quartile 2 | 28.44 to <37.25 | 48 | 1.26 (0.76–2.10) | 1.26 (0.72–2.23) | 78 | 1.07 (0.76–1.50) |
Quartile 3 | 37.25 to <46.90 | 40 | 1.46 (0.86–2.50) | 1.39 (0.74–2.61) | 79 | 0.90 (0.62–1.30) |
Quartile 4 | ≥46.90 | 45 | 1.30 (0.75–2.27) | 1.29 (0.70–2.39) | 84 | 1.05 (0.73–1.50) |
P for trend | 0.4 | 0.6 | 0.9 | |||
Pack-Years > 0 | Range | Events | HR (95%CI) | HR (95%CI) | Events | HR (95%CI) |
Quartile 1 | <28.44 | 16 | Ref | Ref | 25 | Ref |
Quartile 2 | 28.44 to <37.25 | 32 | 1.25 (0.74–2.15) | 1.20 (0.67–2.13) | 47 | 1.11 (0.72–1.70) |
Quartile 3 | 37.25 to <46.90 | 41 | 1.44 (0.82–2.53) | 1.38 (0.75–2.56) | 68 | 1.17 (0.76–1.80) |
Quartile 4 | ≥46.90 | 44 | 1.81 (1.04–3.16) | 1.88 (0.75–2.55) | 63 | 1.45 (0.94–2.40) |
P for trend | 0.03 | 0.04 | 0.09 | |||
P for interaction | 0.1 | 0.03 | 0.01 |
© 2020 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.; Hsu, F.-C.; Pierce, J.P. Increased Acid-Producing Diet and Past Smoking Intensity Are Associated with Worse Prognoses among Breast Cancer Survivors: A Prospective Cohort Study. J. Clin. Med. 2020, 9, 1817. https://doi.org/10.3390/jcm9061817
Wu T, Hsu F-C, Pierce JP. Increased Acid-Producing Diet and Past Smoking Intensity Are Associated with Worse Prognoses among Breast Cancer Survivors: A Prospective Cohort Study. Journal of Clinical Medicine. 2020; 9(6):1817. https://doi.org/10.3390/jcm9061817
Chicago/Turabian StyleWu, Tianying, Fang-Chi Hsu, and John P. Pierce. 2020. "Increased Acid-Producing Diet and Past Smoking Intensity Are Associated with Worse Prognoses among Breast Cancer Survivors: A Prospective Cohort Study" Journal of Clinical Medicine 9, no. 6: 1817. https://doi.org/10.3390/jcm9061817