Benefits and Precautions in Using B-Type Natriuretic Peptide　― N-Terminal-Pro-B-Type Natriuretic Peptide Conversion Formula ―

B-type natriuretic peptide (BNP) and N-terminal-pro BNP (NT-proBNP) are established biomarkers of heart failure (HF).¹ Previously, because of limitations in both assay development and the Japanese health insurance system, many institutions measured only BNP and not NT-proBNP. In the recent global HF guidelines, angiotensin-receptor neprilysin inhibitor (ARNI) was placed as one of the key drugs for the treatment of HF,¹ and since then the focus on the natriuretic peptide system has increased. This has led many facilities to reevaluate the potential of NT-proBNP, which is less susceptible to ARNI, and to measure it more frequently, requiring the development of a platform that enables the comparison and evaluation of previously obtained data or data obtained from other institutions.

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In cardiomyocytes, mechanostress, ischemia, and various cytokines induce BNP gene expression. ProBNP, the translated product of BNP, is cleaved into BNP and NT-proBNP by a processing enzyme, furin.² Not all proBNP undergoes this cleavage, and non-cleaved proBNP can be secreted and circulate in the blood. The currently available BNP assay is an immunoassay method that sandwiches the BNP molecule between 2 antibodies and simultaneously detects and measures both BNP and proBNP. Generally, 60–70% of venous blood BNP measurements in healthy subjects reflect the proBNP levels.³ Compared with BNP, proBNP has extremely low bioactivity. Albeit the measured BNP levels are high, a predominantly increased percentage of proBNP may not compensate for worsening HF, a known BNP paradox.⁴ Therefore, the percentage of true BNP in the measured BNP value is considered as the important indicator for HF.

So far, several studies have developed formulas for conversion between BNP and NT-proBNP values (Table).^5–14 Many of these are described as simple linear regression equations using the actual or logarithmic BNP values. In 2019, Kasahara et al sought a more accurate tool and developed a new conversion formula that takes into account multiple factors such as age, sex, body mass index, creatinine, hemoglobin, and presence of atrial fibrillation, which affects the BNP values.¹³ The formula is highly accurate and possibly optimal for complementing the missing values in clinical studies. However, the requirement of assessing multiple parameters in this conversion may limit its use in busy clinical settings. To provide a better solution, as shown in this issue of the Journal, Ishihara et al¹⁴ conducted a multicenter retrospective study and proposed a new BNP-NTproBNP conversion formula, which uses only 2 parameters, body mass index and estimated glomerular filtration rate, thus making it more feasible and practical to use in clinical settings.

Table. BNP-NT-proBNP Conversion Formula

Reference	Year	Conversion formula	Derivation cohort, n	Validation cohort, n
Fischer et al5	2001	BNP = 1.198 * NT-proBNP + 1.419	Underlying cardiac disease and suspected HF, 95 and healthy, 50	None
Lainchbury et al6	2003	n/a	Acute dyspnea, 205	None
Yeo et al7	2003	NT-proBNP = 5.99 * BNP + 1107	Undefined, 327	None
Alibay et al8	2005	log10 NT-proBNP = 1.062 * log10 BNP – 0.0029 * CCr	Acute dyspnea, 160	None
Sanz et al9	2006	NT-proBNP (Elecsys assay) = −79 + 12.9 * BNP (Access assay)	Acute dyspnea, 75 and control, 25	None
Cameron et al10	2006	log10 NT-proBNP = 1.22 * log10 BNP + 0.75	Suspected ACS, 420	None
Mair et al11	2008	log10 BNP = 0.8 * log10 NT-proBNP − 0.018	Consecutive samples for routine NT-proBNP determination, 458	None
Curiati et al12	2013	n/a	Consecutive samples, 138	None
Kasahara et al13	2019	NT-proBNP = 10 ^ [2.05 + 0.907 * log10 BNP − 0.00522 * Age + 0.00283 * BMI − 0.00866 * Hb − 0.0422 * CCr + 0.000530 * CCr2 − 0.00000214 * CCr3 − 0.00000278 * max {0, (CCr − 56.5)}3 + 0.00000621 * max {0, (CCr − 72.4)}3 − 0.00000133 * max {0, (CCr − 93.7)}3 + 0.0164 (if women) + 0.194 (if with AF)]	HF, 923	With or at risk of cardiovascular diseases, 1,154
Ishihara et al14	2022	log NT-proBNP = 1.21 + 1.03 * log BNP − 0.009 * BMI − 0.007 * eGFR	HF, CAD, HTN, dyslipidemia, or DM, 6,575	HF, CAD, HTN, dyslipidemia, or DM, 2,819

ACS, acute coronary syndrome; AF, atrial fibrillation; BMI, body mass index; BNP, B-type natriuretic peptide; CAD, coronary artery disease; CCr, creatinine clearance; DM, diabetes mellitus; Hb, hemoglobin; HF, heart failure; n/a, not available; HTN, hypertension; NT-proBNP, N-terminal proBNP.

There are several points to be considered before clinically implementing the use of BNP conversion formulas. Firstly, BNP measurements can be influenced by a variety of pathologies and factors that were not considered in the development of the conversion model. For instance, renal dysfunction, ARNI internal use, aging, atrial fibrillation, comorbid inflammatory disease or cancer, hyperthyroidism, and macro-proBNPemia may increase the measured BNP levels, while obesity, constrictive pericarditis, and pericardial effusion may decrease them.¹⁵ Secondly, differences in the BNP assays may limit the use of conversion formulas. The findings in the present study were derived by combining data obtained from different BNP measures. Finally, it still remains unclear whether the developed conversion equations can be broadly applied to all cohorts. Validation of the conversion formula in a separate cohort can provide better evaluation of the reliability of this formula.

Taken collectively, it is essential to consider the strengths and limitations of the conversion formulas and choose to use them appropriately. Further evidence is required to establish and standardize the utilization of BNP conversion formula in clinical settings.

Disclosures

T.O. received lecture fees from Ono Yakuhin, Novartis, Otsuka, and AstraZeneca, and research grants from Ono Yakuhin, Amgen Astellas, Pfizer, Alnylam, and Alexion (not in connection with the submitted work). T.M. received unrestricted research grants for the Department of Cardiology, Nagoya University Graduate School of Medicine from Astellas, Daiichi-Sankyo, Dainippon Sumitomo, Kowa, MSD, Mitsubishi Tanabe, Boehringer Ingelheim, Novartis, Otsuka, Pfizer, Sanofi-Aventis, Takeda, and Teijin, as well as lecture fees from Bayer, Daiichi-Sankyo, Dainippon Sumitomo, Kowa, MSD, Mitsubishi Tanabe, Boehringer Ingelheim, Novartis, Pfizer, Sanofi-Aventis, Takeda, Astellas, Otsuka, and Teijin. T.M. is a member of Circulation Journal’s Editorial Team.

Acknowledgments / Sources of Funding

None.

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