Reference Ranges for NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) and Risk Factors for Higher NT-proBNP Concentrations in a Large General Population Cohort (2024)

Demographic differences in expected NT-proBNP (N-terminal pro-B-type natriuretic peptide) concentration are not well established. We aimed to establish reference ranges for NT-proBNP and explore the determinants of moderately elevated NT-proBNP under the universal definition of heart failure criteria.

Generation Scotland Scottish Family Health Study (GS:SFHS)

This is a cross-sectional study within the cohort. The recruitment and design of the GS:SFHS has been reported in detail previously.^15,16 During 2006 to 2010, potential participants (aged 35–65 years) were identified at random from collaborating general medical practices in Scotland and invited to participate. Participants were also asked to identify ≥1 first-degree relative aged ≥18 years who would be able to participate. A total of 21 476 participants aged between 18 and 98 years attended a research clinic in different urban areas of Scotland. At the clinic, participants had physical and clinical characteristics (including systolic blood pressure [SBP] and body mass index [BMI]) measured according to a standardised protocol and had a questionnaire administered (https://www.ed.ac.uk/generation-scotland/for-researchers/generation-scotland). Ethical approval for the protocol and written study materials were received from NHS Tayside Research Ethics Committee (REC reference number 05/S1401/89). All participants gave informed consent.

Past medical history, including a diagnosis of diabetes (type 1 or type 2) and heart disease or stroke, was recorded using a self-reported questionnaire. Scottish Index of Multiple Deprivation (SIMD) scores are national composite measures of socioeconomic deprivation and were derived from participant postcodes, with higher scores indicating greater socioeconomic deprivation.¹⁷

Fasting blood samples were taken, according to a standard operating procedure, and serum samples were separated. Biochemistry measures including total cholesterol, HDL (high-density lipoprotein) cholesterol, and creatinine was measured at the time of collection and additional serum aliquots were stored at −80 °C for future biochemical analyses. High sensitivity cTnI (cardiac troponin I) and high sensitivity cTnT (cardiac troponin T) were measured as previously described.¹⁸

Previous Cardiovascular Disease

Classification of previous cardiovascular disease (including heart failure) at recruitment was based on linked data from the Scottish Morbidity Record (SMR01), and self-reported heart disease or stroke. SMR01 data were used to identify patients who had been hospitalized for coronary heart disease, heart failure, or ischemic stroke at any time before their baseline assessment (using International Classification of Disease (ICD)-10 codes I20-I25, I50, I42.0, I42.6, I42.7, I42.9, I11.0, I63, I64, G45). Any participant who had self-reported heart disease or stroke or hospitalized with one of these SMR01 diagnostic codes before their study assessment was excluded.

Measurement of NT-proBNP

NT-proBNP was measured on a cobas e411 analyser (Roche Diagnostics, Basel, Switzerland). The assay was calibrated and quality controlled using the manufacturer’s reagents. Coefficients of variation for NT-proBNP were 4.5 % for the low control and 2.7% for the high control. The study laboratory participated in the National External Quality Assurance Scheme (https://ukneqas.org.uk/) for NT-proBNP during the conduct of the study. The limit of detection of the NT-proBNP assay is set to 10 pg/mL by the manufacturer, and in the current study anything less than the limit of detection was reported as 5 pg/mL for continuous analysis. NT-proBNP measurements were undertaken during a single (second) thaw of stored serum aliquots.

Statistical Analysis

Participants with missing data for NT-proBNP, previous cardiovascular disease, or with no time of baseline appointment available were excluded from analyses. The sex-stratified GS:SFHS NT-proBNP medians, 95th, 97.5th, and 99th centiles along with associated bias-corrected 90% CIs (percentile 90% CIs for the ≥80 years age band) around the estimates were determined by bootstrapping 5000 samples in each age and sex-specific strata. Quantile regression using fractional polynomials was used to further model the relationship between age (and time of study appointment) with the median and 97.5th centile of NT-proBNP.

The observed prevalence of moderately elevated NT-proBNP above the existing clinical rule out thresholds were explored, in age and sex strata of the population, using 125 pg/mL based on the universal definition of heart failure (and the European Society of Cardiology clinical guidelines), as well as 400 pg/mL based on National Institute for Health and Care Excellence guidelines.^1,4 The proportion of participants, above existing rule out thresholds was estimated using single sample proportions and 95% CI from binomial exact estimates. Patterns of the prevalence of sociodemographic and classical cardiovascular risk factors across the distribution of NT-proBNP were explored by splitting NT-proBNP into approximate quintiles (not sex-specific), reserving the top quintile for participants with NT-proBNP above the 125 pg/mL threshold. Risk factors were explored separately by sex across these approximate NT-proBNP quintiles. In a further analysis, the patterns of sociodemographic and cardiovascular risk factors were explored specifically in young males and females (<40 years) by status according to whether or not NT-proBNP was moderately elevated at the ≥125 pg/mL threshold. For these analyses, continuous risk factors were expressed as mean and SD if normally distributed, and median and interquartile interval if skewed. Categorical risk factors were expressed as number and percentages.

Associations of elevated NT-proBNP with sociodemographic and classical cardiovascular disease risk factors were further investigated using logistic regression with robust standard errors. Missing data for classical risk factors were imputed by multiple chained imputations over ten datasets. In these models, the outcome was moderately elevated NT-proBNP ≥125 pg/mL. In a first regression model, age categories (by decade) and sex (with an interaction between age and sex) were used as exposure variables. In a second regression model, diabetes, current smoking, estimated glomerular filtration rate, blood pressure lowering medication use, SBP, BMI, cTnT, total cholesterol, and HDL-cholesterol were added, allowing for a sex interaction with each variable. Cholesterol-lowering medications were not added to the model, as there was no association with elevated NT-proBNP in either sex after adjustment. In an exploratory clustering analyses by family group, the intraclass correlation coefficient for NT-proBNP within families was 0.096 (95% CI, 0.082–0.111), indicating minimal family clustering; this was therefore not considered a factor in any other analyses.

All statistics were performed using STATA version 17.0.

Population Characteristics

Of the 21 476 GS:SFHS participants 19 468 provided a serum sample with sufficient volume for measurement of NT-proBNP, as well as cTnI and cTnT (90.7%). After exclusion of 1110 participants with previous self-reported heart disease or stroke or previous hospitalization for coronary heart disease, heart failure, or ischemic stroke, and exclusion of 2 participants with no data on time of study appointment, there were 18 356 participants included in the study. Mean age was 46.1 years (SD 14.7 years), and 7503 participants (40.9%) were males. The median NT-proBNP was 50 pg/mL (interquartile interval 26, 90 pg/mL). Detectable (>10 pg/mL) levels of NT-proBNP were found in 17 071 participants (93.0%), and NT-proBNP was detectable in 97.8% of females and 86.0% of males.

Diurnal Variation of NT-proBNP

In an unadjusted model there was some apparent evidence of diurnal variation of NT-proBNP depending on the hour of the study assessment; however, this trend attenuated toward the null after adjustment for age and sex (Figure ​(Figure1).1). This is primarily because participants attending appointments in the middle of the day tended to be in the older age categories (Table S1). Since subsequent models reported here are stratified by age and sex, or adjust for it, diurnal variation was not considered as a covariate in other analyses.

Prevalence of Moderately Elevated NT-proBNP

Moderately elevated NT-proBNP (≥125 pg/mL) was detected in 2686 participants overall, specifically in 19.8% of females and in 7.1% of males. At the age band of <30 years, 9.8% of females had NT-proBNP ≥125 pg/mL, rising to 76.5% at age ≥80 years (Figure ​(Figure2;2; Table S2). Approximately 10% or more of females at every age strata had elevated NT-proBNP (Figure ​(Figure2;2; Table S2). In males at age <30 years, 1.4% were above the ≥125 pg/mL threshold, rising to 7.6% at 50 to 59 years, and 81.0% at age ≥80 years.

Using the higher threshold of 400 pg/mL (advocated by National Institute for Health and Care Excellence as a rule-out threshold), <1% of males and females had NT-proBNP ≥400 pg/mL in age groups up to 40 to 49 years, and 30.4% of females and 33.3% of males had NT-proBNP above this threshold in the ≥80 years age category (Figure ​(Figure2;2; Table S2).

Reference Ranges for NT-proBNP

In males, median (97.5th centile) NT-proBNP concentration at age <30 years was 21 (104) pg/mL, rising to 38 (195) pg/mL at 50 to 59 years, and 281 (6792) pg/mL at ≥80 years. In females, median NT-proBNP at age <30 years was 51 (196) pg/mL, 66 (299) pg/mL at 50 to 59 years, and 240 (2704) pg/mL at ≥80 years (Table ​(Table1).1). Notably, median NT-proBNP was only higher in males than in females in the 80+ age category (Table ​(Table1).1). Males and females both generally had lower estimated glomerular filtration rate (eGFR) in the 80+ age category, which may partially explain some of the elevation in NT-proBNP in older participants (Table S3). However, reference ranges were broadly similar when restricting the cohort further to those with eGFR>60 mL/minute per 1.73 m² (Table S4).

Continuous models of the median and 97.5th centile of NT-proBNP using quantile regression showed similar trends to the categorical age model, with generally higher levels of NT-proBNP in younger females, a slow rise in expected levels up to age 50 years, and then a more rapid rise in both sexes beyond the age of 50 (Figure ​(Figure3).3). Predicted levels of NT-proBNP were generally higher in participants with poor renal function (Figure S1). Predicted levels of NT-proBNP were slightly higher in those with BMI <25 kg/m², particularly among females (Figure S2).

Sociodemographic and Cardiovascular Risk Factors Across Quintiles of NT-proBNP

Among female participants, those in the highest approximate quintile (≥125 pg/mL) for NT-proBNP were generally older, had higher SBP despite more frequently taking blood pressure lowering medication, had lower eGFR, higher cTnT and cTnI, and were more likely to be using cholesterol lowering medication. However, there was also a U-shaped association of NT-proBNP with diabetes and with BMI; females in the lowest NT-proBNP quintile were most likely to have diabetes and had the highest BMI. Females in the highest quintile also had slightly higher HDL-cholesterol and were less likely to be current smokers than females in the first quintile (Table ​(Table2).2). Patterns were similar in males, although males in the highest quintile had slightly higher BMI and were more likely to have diabetes than males in the lowest quintile (Table ​(Table33).

Associations of Sex With Moderately Elevated NT-proBNP (≥125 pg/mL)

Using a model adjusting for age (and allowing for an interaction of age with sex), the odds ratio for moderately elevated NT-proBNP (≥125 pg/mL) was higher in females than in males (OR, 7.54 [95% CI, 4.72–12.1]). After adjusting for a panel of sociodemographic and cardiovascular risk factors (age, diabetes, SBP, total cholesterol, HDL-cholesterol, eGFR, smoking, blood pressure medication use, BMI and cTnT [plus allowing for sex interactions with each risk factor]) females still had higher odds of an elevated NT-proBNP (OR, 9.48 [95% CI, 5.60–16.1]).

Young males (<40 years) with moderately elevated NT-proBNP were more likely to be smokers and had higher cTnI and cTnT but also had lower BMI than young males who did not have moderately elevated NT-proBNP (Table ​(Table4).4). Among young females (<40 years), there was only a borderline trend that those with moderately elevated NT-proBNP had a slightly higher BMI. In general, in both sexes, young people with moderately elevated NT-proBNP had a generally low risk cardiovascular profile, including generally moderate cholesterol levels and blood pressure, good renal function, and low levels of cTnT and cTnI (Table ​(Table44).

Associations of Age and Other Cardiovascular Risk Factors With Moderately Elevated NT-proBNP

Exploring different associations of cardiovascular risk factors with moderately elevated NT-proBNP in the sexes, age category was positively associated with moderately elevated NT-proBNP in both sexes after adjusting for other risk factors, although the association was stronger in males (P_interaction <0.001; Figure ​Figure4).4). Smoking was associated with increased odds of moderately elevated NT-proBNP in both sexes although the association was borderline stronger among males (P_{sex interaction}=0.07). Use of blood pressure–lowering medication, SBP, and higher cTnT was associated with increased odds of moderately elevated NT-proBNP in both sexes to a similar extent. Higher eGFR was associated with lower odds of moderately elevated NT-proBNP in both sexes. Total cholesterol was inversely and HDL cholesterol positively associated with odds of moderately elevated NT-proBNP. BMI showed a borderline inverse association with moderately elevated NT-proBNP in both sexes. Diabetes was inversely associated with odds of elevated NT-proBNP in females but was not associated with odds of elevated NT-proBNP in males (P_{sex interaction}=0.007). This divergence in the association of diabetes with NT-proBNP between the sexes was still observed in a sensitivity analysis when BMI, troponin, total cholesterol, and HDL cholesterol were eliminated from the adjustment model.

Acknowledgments

The authors thank Philip Stewart, Elaine Butler, Emma Dunning‚ and Josephine Cooney (University of Glasgow, United Kingdom) for excellent technical support and Liz Coyle, University of Glasgow, for her assistance with manuscript preparation. The authors are grateful to all the families who took part, the general practitioners and the Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, health care assistants, and nurses.

Sources of Funding

Roche Diagnostics supported this study through provision of free reagents and a grant. Generation Scotland received support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Dr Mills is supported by a British Heart Foundation Senior Clinical Research Fellowship (FS/16/14/32023). Dr Januzzi is supported in part by the Hutter Family Professorship Chair. Drs McMurray and Sattar are supported by British Heart Foundation Centre of Research Excellence Grant RE/18/6/34217. D.M. Kimenai was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation‚ and the Wellcome Trust. The funders had no role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, and in the preparation, review, or approval of the article.

Disclosures

Dr Welsh reports grant income from Roche Diagnostics within this work and grant income from AstraZeneca, Boehringer Ingelheim, and Novartis, and speaker fees from Novo Nordisk, outside the submitted work. Dr Sattar has received grant and personal fees from AstraZeneca, Boehringer Ingelheim, and Novartis; grant from Roche Diagnostics; and personal fees from Abbott Laboratories‚ Afimmune, Amgen, Eli Lilly, Hanmi Pharmaceuticals, Merck Sharp & Dohme, Novo Nordisk, Pfizer, and Sanofi outside the submitted work. Dr Januzzi is a Trustee of the American College of Cardiology; is a board member of Imbria Pharmaceuticals; has received grant support from Abbott Diagnostics, Applied Therapeutics, Innolife, and Novartis; has received consulting income from Abbott Diagnostics, Boehringer Ingelheim, Janssen, Novartis, and Roche Diagnostics; and participates in clinical end point committees/data safety monitoring boards for AbbVie, Siemens, Takeda, and Vifor. Dr McMurray reports payments to his employer, University of Glasgow, for work on clinical trials, consulting, lecturing, and other activities from Alnylam, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardurion, Cytokinetics, Dal-Cor, GlaxoSmithKline, Ionis, KBP Biosciences, Novartis, Pfizer, and Theracos, as well as personal lecture fees from Abbott, Hikma, Sun Pharmaceuticals, and Servier. Dr Mills has received honoraria from Abbott Diagnostics, Siemens Healthineers, and Singulex, and the University of Edinburgh has received research grants from Abbott Diagnostics and Siemens Healthineers. All other authors declare no conflicts.

Supplemental Material

Tables S1–S4

Figures S1 and S2

This manuscript was sent to John C. Burnett Jr, MD, Guest Editor, for review by expert referees, editorial decision, and final disposition.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCHEARTFAILURE.121.009427.

For Sources of Funding and Disclosures, see page 966.

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