The long-term impact of elevated blood pressure on mortality outcomes has been recently revisited due to proposed changes in cut-offs for hypertension. This study aimed at assessing the association between high blood pressure levels and 10-year mortality using the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7) and the American College of Cardiology and the American Heart Association (ACC/AHA) 2017 blood pressure guidelines.Data analysis of the PERU MIGRANT Study, a prospective ongoing cohort, was used. The outcome of interest was 10-year all-cause mortality, and exposures were blood pressure categories according to the JNC-7 and ACC/AHA 2017 guidelines. Log-rank test, Kaplan-Meier and Cox regression models were used to assess the associations of interest controlling for confounders. Hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated.A total of 976 records, mean age of 60.4 (SD: 11.4), 513 (52.6%) women, were analyzed. Hypertension prevalence at baseline almost doubled from 16.0% (95% CI 13.7%–18.4%) to 31.3% (95% CI 28.4%–34.3%), using the JNC-7 and ACC/AHA 2017 definitions, respectively. Sixty-three (6.4%) participants died during the 10-year follow-up, equating to a mortality rate of 3.6 (95% CI 2.4–4.7) per 1000 person-years. Using JNC-7, and compared to those with normal blood pressure, those with pre-hypertension and hypertension had 2.1-fold and 5.1-fold increased risk of death, respectively. Similar mortality effect sizes were estimated using ACC/AHA 2017 for stage-1 and stage-2 hypertension.Blood pressure levels under two different definitions increased the risk of 10-year all-cause mortality. Hypertension prevalence doubled using ACC/AHA 2017 compared to JNC-7. The choice of blood pressure cut-offs to classify hypertension categories need to be balanced against the patients benefit and the capacities of the health system to adequately handle a large proportion of new patients. Worldwide, the number of adults living with hypertension has increased from 563 million in 1975 to 1.13 billion in 2015, and the prevalence of hypertension in 2015 was estimated to be 24.1% and 20.1% in men and women, respectively.Ischemic heart disease and cerebrovascular disease are the first and second cause of death globally. and those with pre-hypertension are more likely to develop hypertension and its consequences. In 2017, as part of the ongoing review process of full guidelines commissioned in about 6-year cycles, the American College of Cardiology and the American Heart Association (ACC/AHA 2017) changed the proposed cut-off points used for defining hypertension, and for instance, included part of the pre-hypertension cases as hypertension (known as stage 1 hypertension). The adoption of the ACC/AHA 2017 guidelines may produce changes in the proportion of cases with hypertension as reported for the US general population by the Systolic Blood Pressure Intervention Trial (SPRINT) Study, where the prevalence of hypertension almost doubled from 49.7% using JNC-7 to 80.1% by ACC/AHA 2017. Similar changes in hypertension prevalence have been described in different countries.–Levels of blood pressure before the development of hypertension are known as pre-hypertension according to the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (known as JNC-7),– Whilst the association between blood pressure levels, defined by JNC-7, and mortality has been well described, the evidence of the impact of the new definitions of hypertension on all-cause mortality in resource-constrained settings remains limited., Therefore, long-term studies involving populations from low- and middle-income countries (LMICs) are needed given that raised blood pressure is a major contributor to the global burden of disease.Different reports associate arterial mean and blood pressure levels with all-cause mortality and cardiovascular mortality.This study aimed at assessing whether the levels of blood pressure, using two different guidelines, JNC-7 and ACC/AHA 2017, are associated with 10-year mortality using an ongoing Peruvian cohort study. The baseline of the study was conducted in 2007–2008 and follow-ups were carried out in 2012–2013, 2015–2016, and 2018. For this analysis, data from the baseline assessment and 2018 follow-up were used.Data analysis of the PERU MIGRANT Study, a prospective ongoing cohort conducted enrolling three different population groups: rural, rural-to-urban migrants, and urban dwellers was carried out. Pregnant women or potential participants unable to understand procedures and consent were excluded.Las Pampas de San Juan de Miraflores, a highly urbanized area in the city of Lima, was selected as the urban environment, whereas San Jose de Secce, a district of Ayacucho in the highlands, was selected as the rural site. Individuals who were ≥30 years of age and habitual residents in the selected study sites were invited to participate at baseline. Rural dwellers were enrolled in San Jose de Secce, while urban residents and rural-to-urban migrants were recruited from Las Pampas de San Juan de Miraflores in Lima.Participants were randomly selected using an age-  and sex- stratified sampling approach, using the most up-to-date census in the study area. San Jose de Secce (Ayacucho) was the area chosen for the selection of rural dwellers. Migrants were those born in Ayacucho but living in Las Pampas de San Juan de Miraflores (Lima) at the time of the study enrolment. Finally, urban dwellers were those permanently living in the same area.Power estimations were based on major risk factors in Huaraz (highlands) and Lima. The baseline study aimed at recruiting 1000 participants . Comparing Lima and highlands groups, the study had 84% power to detect a difference in the prevalence of hypertension (33% vs. 19.5%) enrolling 200 subjects in each group. Such power was 81% in the case of type 2 diabetes (7.6% versus 1.3%).Outcome The outcome of interest was the time until an event, defined as the time, in years, lapsed from the baseline assessment (2007–2008) to death or censorship during follow-up. Information about vital status and date of death (or censoring) was obtained via assessment of the National Record of Identification and Civil Status (RENIEC (Spanish acronym)) conducted in 2018.Exposure The exposure variable was hypertension-related categories using measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP) under two different definitions, JNC-7 and ACC/AHA 2017. Under the JNC-7 definition, individuals were split into three categories: normal (SBP < 120 mm Hg and DBP <80 mm Hg without using specific medication), pre-hypertension (SBP 120–139 mm Hg and DBP 80–89 mm Hg without anti-hypertensive therapy), and hypertension . On the other hand, under the ACC/AHA 2017 definition, participants were split into four categories: normal (same as those in JNC-7), elevated blood pressure , stage 1 hypertension (SBP 130–139 mm Hg and DBP 80–89 mm Hg without treatment), and stage 2 hypertension (same as those with hypertension in the JNC-7).Covariates Other variables included as potential confounders in the analysis were: age , sex (men vs. women), education level (less than seven vs. more than seven years), socioeconomic status, defined by using an assets index and then split in tertiles , and population group . In addition, behavioural variables were also included: daily smoking, self-reported, based on the consumption of at least one cigarette per day; alcohol use, defined according to the self-reported consumption of six or more beers  on the same occasion at least once a month (low vs. high); and physical activity level, based on the short version of the International Physical Activity Questionnaire (IPAQ) and split into low and moderate/high (www.ipaq.ki.se). Finally, total cholesterol (< 200 mg/dL and ≥ 200 mg/dL) and type 2 diabetes, defined as fasting glucose ≥ 126 mg/dL or previous diagnosis made by a physician, were also included. Fieldworker’s training included application of informed consent and questionnaires, and the attainment of clinical measurements using appropriate and calibrated methods. Blood pressure was measured in seated position after a resting period of five minutes. Measures were done by triplicate using an automated device (OMRON HEM-780) and the average of the second and third measurements was used to define hypertension. Laboratory assessments were performed on venous samples taken in the morning after a minimum of eight hours (maximum 12 hours) of fasting. Total cholesterol was measured in serum, and fasting glucose was measured in plasma using a Cobas® 6000 Modular Platform automated analyser and reagents supplied by Roche Diagnostics.Recruitment of participants was conducted by community health workers utilizing standardized tools. Questionnaires were based on the World Health Organization (WHO) STEPwise approach to surveillance (STEPs), validated in a pilot study and previously published.STATA 16 for Windows  was used for statistical analysis. An open-access alternative that can provide an equivalent function is theRstats package . Sociodemographic, lifestyle behavioural and anthropometric variables were described according to each definition of blood pressure levels (JNC-7 and ACC/AHA 2017) using the Chi-squared test. Variables were also described according to vital status using the Log-rank test. A plot of the Kaplan-Meier estimator was used to evaluate the assumption of proportional hazards graphically, whereas such assessment was done in post-hoc analysis using the Schoenfeld residuals. Crude and adjusted Cox regression models were used to estimate the strength of the association between variables of interest , reporting hazard ratios (HR) and 95% confidence intervals (95% CI). Potential confounders were selected under epidemiological criteria, based on literature and available in the dataset. Since the selection of participant's sample was stratified by age , such variable was included in that form in adjusted models. Using full-adjusted models, Akaike and Bayesian information criteria (AIC and BIC). The protocol for this secondary data analysis was approved by the ethics committee at Universidad Peruana de Ciencias Aplicadas  in Lima, Peru.The original PERU MIGRANT Study was approved by Institutional Review Boards (IRB) at Universidad Peruana Cayetano Heredia  in Peru and London School of Hygiene and Tropical Medicine  in the UK. Follow-up was approved by the IRB at the UPCH only. Written informed consent was given by study participants prior to starting research activities. Permission was obtained to use personal identifiers to link participant’s information with vital status records; and only deidentified and anonymized data was used for publication.A total of 989 participants were enrolled at baseline, but 13 (1.3%) were excluded as no mortality information was available at the end of the study. Thus, only 976 were included in further analyses. Of them, 196 (20.1%) were rural, 582 (59.6%) migrants, and 198 (20.3%) were urban dwellers, have a mean age of 60.4 (SD: 11.4), and 513 (52.6%) were women.Hypertension prevalence at baseline almost doubled from 16.0% (95% CI 13.7%–18.4%) to 31.3% (95% CI 28.4%–34.3%) using the JNC-7 and ACC/AHA 2017 guidelines, respectively. In both definitions, high blood pressure levels were more frequent among males, older subjects, migrant and urban dwellers, as well as those with obesity and those with type 2 diabetes mellitus .A total of 63 (6.4%) participants died during the 10-year follow-up with 9992.6 person-years of follow-up and a mortality rate of 3.6 (95% CI 2.4–4.7) per 1000 person-years. Men, older individuals, those with lower education, those with lower socioeconomic status, and having type 2 diabetes mellitus had an increased risk of 10-year mortality .Kaplan-Meier plots were created using the exposures of interest . There wWhen comparing adjusted models, AIC and BIC were very similar , highlighting no difference between models.According to our findings, high blood pressure levels increased the risk of 10-year all-cause mortality, and our estimates showed similar long-term effect sizes across blood pressure categories using two different guidelines. As countries move into better universal health coverage, primary prevention and access to medications should be secured to reduce the health burden of raised blood pressure. However, how countries prepare and secure resources to successfully meet the challenges of hypertension will depend on how this is defined. There was a remarkable difference on hypertension prevalence depending on whether the JNC-7 or the ACC/AHA 2017 definition was followed, but the latter definition would avoid approximately 20% more deaths than the JNC-7 guideline. This carries relevant implications and repercussions for patients and health systems. Should the ACC/AHA 2017 definition be adopted because this will require securing treatment for a substantial larger population with the costs and challenges it entails. However, it can be argued that information came from a clinical trial, which may have included more high-risk patients than in the general population; also, participants in the SPRINT Study were followed-up for 3.3 years. Conversely, we conducted a population-based 10-year follow-up study, advancing the evidence for the general population.In the US, the SPRINT Study, conducted in 2010, reported that the ACC/AHA 2017 definition significantly increased the prevalence of patients with hypertension and identified more patients who will experience adverse cardiovascular events. the ACC/AHA 2017 stage 1 hypertension was associated with an increased risk of cardiovascular disease mortality; notably, another cohort study with participants recruited from 1997 to 1999, and with 20 years of follow-up, did not find such association in rural dwellers in the same country. The difference could be explained by different risk factor profiles in rural areas, or presumably lower levels of risk factors over twenty years ago. Using the National Health and Nutrition Examination Surveys between 2003 and 2014, a study found that the ACC/AHA 2017 guidelines would increase the proportion of stroke survivors in the US compared to the JNC-7 definition. Thus, there is a potential benefit of applying the ACC/AHA 2017 guidelines, although this needs to be verified in different population groups.Because of data availability, we could not assess cardiovascular mortality; nonetheless, it is likely that we would have seen a similar – or even larger – effect as the one herein reported for all-cause mortality. In a pooled analysis of prospective cohorts conducted in China, starting from 1996 to 2010,, Thus, cases of stage 1 hypertension, previously classified as pre-hypertension in JNC-7, will start treatment with an initial anti-hypertensive drug if estimated 10-year cardiovascular risk is ≥ 10%, but CV risk scores have showed poor concordance in Latin America populations; whereas those in stage 2 hypertension would start with two anti-hypertensive drugs.– In support of these concerns, a study showed that hypertension prevalence would increase by 40% in the US. Similarly in Peru, using information from a population-based survey, the prevalence of hypertension would increase from 14% to 32%. Peru is a middle-income country with a fragile and fragmented healthcare system, with poor response to the challenges of chronic conditions. Increasing the number of people with hypertension may benefit those with blood pressure levels in the range 130–139/80–89 mm Hg, but would represent a major investment  so that these patients can receive adequate treatment, management, and follow-up. Treatment must be guaranteed for hypertension (using JNC-7) and stage 2 hypertension (ACC/AHA 2017) to reduce mortality risk. If this effect can be seen in pre-hypertension (JNC-7) and stage 1 hypertension (ACC/AHA 2017) should be further assessed. Thus, a thoroughly planned and balanced policy would be needed to provide care to those who most needed it. From the clinical perspective, a tailored follow-up of those individuals with pre-hypertension (or stage 1 hypertension) may be required to decide appropriate treatment, even high-risk stratification may be considered. In addition to that, a combination of population-wide interventions may be also needed.The ACC/AHA 2017 guidelines radically proposed to change definitions of blood pressure levels, with pre-hypertension split into two categories: elevated blood pressure and stage 1 hypertension. Multiple authors have questioned this change, and pinpointed that hypertension prevalence would increase, pharmacotherapy of hypertension will start at a lower blood pressure level, and the threshold for hypertension control will decrease. the beginning of antihypertensive therapy will certainly have advantages, especially the reduction of patient’s complications and mortality. However, there will be an increase of primary care costs, which can be more deleterious in resource-constrained settings. A recent study conducted in the US has estimated that reaching the goals of the ACC/AHA 2017 guidelines will reduce 610,000 cardiovascular events and avoid 334,000 total deaths per year among adults 40 years and older. Nevertheless, the potential increase of adverse events related to the use of anti-hypertensive drugs should be also considered as well as a substantial number of hypertension cases giving up or taking medication irregularly. Thus, although the adoption of ACC/AHA 2017 guidelines may seem pertinent in term of complications and mortality reduction, Peru as well as other low- and middle-income countries, may not be prepared for this scenario.As the risk of coronary artery disease and stroke rise progressively increases as blood pressure increases above 115/75 mm Hg,This study takes advantage of an ongoing population-based cohort study conducted in a resource-constrained setting with three different population groups to evaluate the impact of two definitions of high blood pressure levels and 10-year mortality. However, this study has some limitations that should be highlighted. First, due to data availability, this study analysed all-cause mortality as outcome instead of assessing cardiovascular mortality. Since blood pressure increases the risk of cardiovascular events and mortality, we can speculate that the association of interest will be stronger and probably did not vary between hypertension definitions as in our analysis. Second, some variables, relevant to understand the relationship between hypertension and all-cause mortality, were not available . Third, although left truncation can be present as subjects <30 years were excluded, the potential effect of such limitation may be negligible as the prevalence of hypertension is low in that age group. Fourth, selection bias can be an issue as studied population groups were selected in specific sites and may not be totally representative of the general population. In addition, measurement bias may be present. However, standard procedures were used to assess blood pressure levels. Finally, we did not assess the potential effect of anti-hypertensive drugs on mortality due to limited sample size.Blood pressures levels under two different definitions increased the risk of 10-year all-cause mortality. Hypertension prevalence doubled using the ACC/AHA 2017 compared to the JNC-7 definition. The choice of blood pressure cut-offs to classify hypertension categories need to be balanced against the patient’s benefit and the capacities of the health system to adequately handle a large proportion of new patients. Cardiovascular disease prevention, and, in particular, the prevention of blood pressure-related mortality, will benefit from the estimates reported in this study to adequately inform local decision making, which in addition to disease burden should recognize balance benefits and risks within existing capacities to secure and guarantee adequate and effective treatment for all the new patients with raised blood pressure.https://doi.org/10.6084/m9.figshare.16811350.v3.Figshare: Underlying data for ‘Blood pressure and 10-year all-cause mortality: Findings from the PERU MIGRANT Study’, ‘PERU MIGRANT Study’,-PERU MIGRANT Dataset .csv-Dictionary.txtThis project includes the following underlying data:Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of theAida Hidalgo-Benites: conceptualization, data curation, formal analysis, investigation, methodology, writing original draft, and writing review and editing.Valeria Senosain-Leon: conceptualization, data curation, formal analysis, investigation, methodology, writing original draft, and writing review and editing.Rodrigo M. Carrillo-Larco: data curation, formal analysis, investigation, methodology, validation, and writing review and editing.Andrea Ruiz-Alejos: data curation, formal analysis, funding acquisition, investigation, methodology, and writing review and editing.Robert H. Gilman: funding acquisition, investigation, methodology, supervision, validation, and writing review and editing.Liam Smeeth: funding acquisition, investigation, methodology, supervision, validation, and writing review and editing.J. Jaime Miranda: conceptualization, funding acquisition, investigation, methodology, supervision, validation, and writing review and editing.Antonio Bernabe-Ortiz: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, supervision, and writing review and editing. No further comments.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Pharmacy, Social and Administraive pharmacyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors have responded and acted well to the points and comments raised to the previous version of the manuscript. Hence, the revised paper has been satisfactorily improved and modified and we have no further comments.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Biostatistics, Epidemiology, Diabetes, Chronic DiseasesWe confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Sample Size and Generalizability: While the study provides valuable insights, it is important to ensure that the sample size and characteristics of the study population are representative of the broader population. A larger and more diverse sample could enhance the generalizability of the findings.Confounding Factors: The study controlled for confounding factors, but it is essential to consider potential unmeasured confounders that might affect the relationship between blood pressure and mortality. A more comprehensive assessment of confounding variables, including lifestyle factors and comorbidities, would strengthen the study.Long-Term Follow-Up: Ten years is a substantial follow-up period, but extending the observation period further could provide additional insights into the relationship between blood pressure and long-term mortality.Clinical Implications: The authors should provide recommendations for healthcare practitioners based on their findings. This could include guidance on blood pressure management strategies, especially considering the increased prevalence of hypertension when using ACC/AHA 2017 guidelines.Discussion of Potential Biases: The study should address potential biases, such as selection bias or measurement bias, and explain how these were minimized or managed.Public Health Impact: The study mentions the capacity of healthcare systems to manage a larger number of hypertensive patients. A discussion of the potential public health impact of increased hypertension prevalence and its associated resource implications would be valuable.I encourage the author to address the following:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Pharmacy, Social and Administraive pharmacyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Sample Size and Generalizability: While the study provides valuable insights, it is important to ensure that the sample size and characteristics of the study population are representative of the broader population. A larger and more diverse sample could enhance the generalizability of the findings.Response: This is a secondary analysis of an existing dataset. The idea of the original study was to compare the cardiometabolic profile of three different populations . Even, we tried to take a random sample of participants for each population group, such selection was done in specific study sites using an age- and sex-stratification approach. Based on that, it is not possible to increase the sample size of the study.Confounding Factors: The study controlled for confounding factors, but it is essential to consider potential unmeasured confounders that might affect the relationship between blood pressure and mortality. A more comprehensive assessment of confounding variables, including lifestyle factors and comorbidities, would strengthen the study.Response: We agree with the reviewer. Our adjusted model was built using epidemiological criteria and information available in the dataset. As a result, we did not include variables, important to control the model, but not present in the database. We have clarified that in the Statistical analysis section. In addition, we have added a comment about that in the Strengths and Limitations section:“Second, some variables, relevant to understand the relationship between hypertension and all-cause mortality, were not available .”Long-Term Follow-Up: Ten years is a substantial follow-up period, but extending the observation period further could provide additional insights into the relationship between blood pressure and long-term mortality.Response: As pointed out by the reviewer, extending observation period would be ideal, but currently, that is not possible. Available information was used for this manuscript.Clinical Implications: The authors should provide recommendations for healthcare practitioners based on their findings. This could include guidance on blood pressure management strategies, especially considering the increased prevalence of hypertension when using ACC/AHA 2017 guidelines.Response: We would like to add a comment to provide recommendation for healthcare practitioners. However, this is an observational study looking at the association between two different guidelines for hypertension definition and all-cause mortality, and not a clinical trial to do decision making. Nevertheless, we have included a couple of sentences have been added:“From the clinical perspective, a tailored follow-up of those individuals with pre-hypertension may be required to decide appropriate treatment, even high-risk stratification may be considered. In addition to that, a combination of population-wide interventions may be also needed.”Discussion of Potential Biases: The study should address potential biases, such as selection bias or measurement bias, and explain how these were minimized or managed.Response: Thanks for the suggestion. We have added that in the Strengths and Limitations section:“Fourth, selection bias can be an issue as studied population groups were selected in specific sites and may not be totally representative of the general population. In addition, measurement bias may be present. However, standard procedures were used to assess blood pressure levels.”Public Health Impact: The study mentions the capacity of healthcare systems to manage a larger number of hypertensive patients. A discussion of the potential public health impact of increased hypertension prevalence and its associated resource implications would be valuable. Response: We have discussed this point in our manuscript. We have extended some topics:“Peru is a middle-income country with a fragile and fragmented healthcare system, with poor response to the challenges of chronic conditions. Increasing the number of people with hypertension may benefit those with blood pressure levels in the range 130–139/80–89 mm Hg, but would represent a major investment  so that these patients can receive adequate treatment, management, and follow-up.” The authors have responded and acted well to the points and comments raised to the previous version of the manuscript. However, we still have some open questions and remarks. 1. We do now understand the age cut-off at year 30 and thank the authors for clarification. Nonetheless, we are wondering how the new categorization is justified. With regards to blood pressure and mortality, we would think that particularly the age group 60+ might be at high risk and thus, more detailed analysis  might be of interest. We kindly ask the authors for more details on the stratification choice. Overall though, we still recommend including age as a covariate measured on a continuous scale to improve the power of the analysis. 2. Current guidelines used to report the results of observational studies such as STROBE clearly state that inferential measures should not be used for description of the study population. In line with that, we strongly suggest to delete all p-values from tables 1-3 and to simply describe the characteristics of the study participants. Further, and also as advanced by current guidelines, the use of p-values is not an appropriate criterion for selecting which confounders to adjust for in the analysis. Consequently, we advocate that any statement about potential associations based on single p-values should be avoided. 3. We thank the authors for rewriting the Statistical analysis section where they refer to the Kaplan-Meier estimator. In the text it now reads that the evaluation of the proportional hazards assumption is based on visual inspection of a graph. For higher transparency and better understanding, we would suggest to include the graph in the article or as supporting material.  4. We are pleased to see that the authors followed our recommendation and changed the term “bivariate model”. Though, the term “bivariable model” is still somewhat misleading and rather uncommon to use. We would suggest to simply refer to an “association”. 5. As suggested by the authors, we have reviewed the Public health relevance section for more information on how their study provides input e.g. for local decision making or public health implications. From our point of view, the article still lacks precise guidance in which way the estimates/results from the study presented in the article can be used for decision making with respect to benefits and risks for patients with newly diagnosed hypertension. Consequently, we highly recommend that the authors explain the implications of their study in more detail and that they provide concrete ideas of possible future activities. 6. In our first review we noted that the authors were not consistent in their writing and with the vocabulary used in the tables, e.g. "stage 1" vs. "stage-1" vs. "stage I" (same applies for “stage 2”). In the reviewed article we still find inconsistencies, particularly comparing the Public health relevance section with the rest of the text.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Biostatistics, Epidemiology, Diabetes, Chronic DiseasesWe confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. 1. We do now understand the age cut-off at year 30 and thank the authors for clarification. Nonetheless, we are wondering how the new categorization is justified. With regards to blood pressure and mortality, we would think that particularly the age group 60+ might be at high risk and thus, more detailed analysis  might be of interest. We kindly ask the authors for more details on the stratification choice. Overall, though, we still recommend including age as a covariate measured on a continuous scale to improve the power of the analysis.Response: The categorization of the age variable is due to the fact that the sampling approach was stratified by age in that way . For instance, even we can adjust for that variable in different forms , the best way would be to adjust for the form the variable was stratified. This was explained in the “Settings and participants” subheading in the Methods section. In addition, this was added in the Statistical analysis subheading:Potential confounders were selected under epidemiological criteria. Since the selection of the participant’s sample was stratified by age , such variable was included in that form in adjusted models.” “2. Current guidelines used to report the results of observational studies such as STROBE clearly state that inferential measures should not be used for description of the study population. In line with that, we strongly suggest to delete all p-values from tables 1-3 and to simply describe the characteristics of the study participants. Further, and also as advanced by current guidelines, the use of p-values is not an appropriate criterion for selecting which confounders to adjust for in the analysis. Consequently, we advocate that any statement about potential associations based on single p-values should be avoided.Response: We have deleted p-values from tables 1 to 3 as suggested. We have not selected confounders based on p-values, but in an epidemiological approach (pre-specified model).3. We thank the authors for rewriting the Statistical analysis section where they refer to the Kaplan-Meier estimator. In the text it now reads that the evaluation of the proportional hazard assumption is based on visual inspection of a graph. For higher transparency and better understanding, we would suggest to include the graph in the article or as supporting material.Response:We have included the figure in the manuscript as requested. We believe the figure does not add more information. Thus, we think the figure should be included in online material but F1000Research does not accept supplemental material. We leave the final decision to include the figure to the reviewer and the editor.4. We are pleased to see that the authors followed our recommendation and changed the term “bivariate model”. Though, the term “bivariable model” is still somewhat misleading and rather uncommon to use. We would suggest to simply refer to an “association”.Response: We have deleted the word bivariable from the text of the manuscript.5. As suggested by the authors, we have reviewed the Public health relevance section for more information on how their study provides input e.g. for local decision making or public health implications. From our point of view, the article still lacks precise guidance in which way the estimates/results from the study presented in the article can be used for decision making with respect to benefits and risks for patients with newly diagnosed hypertension. Consequently, we highly recommend that the authors explain the implications of their study in more detail and that they provide concrete ideas of possible future activities.Response: This is an observational study looking at the association between two different guidelines for hypertension definition and all-cause mortality, and not a clinical trial to do decision making. However, according to our results, we have added some lines:“Treatment must be guaranteed for hypertension (using JNC-7) and stage 2 hypertension (ACC/AHA 2017) to reduce mortality risk. If this effect can be seen in pre-hypertension (JNC-7) and stage 1 hypertension (ACC/AHA 2017) should be further assessed.”6. In our first review we noted that the authors were not consistent in their writing and with the vocabulary used in the tables, e.g. "stage 1" vs. "stage-1" vs. "stage I" (same applies for “stage 2”). In the reviewed article we still find inconsistencies, particularly comparing the Public health relevance section with the rest of the text.Response: We have reviewed through manuscript, tables and figures to keep consistency. In the manuscript at hand, the authors report results from a study evaluating the association between blood pressure and all-cause mortality. They aim to investigate whether there are differences in the association using two different definitions of hypertension: The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7) and the more recent guideline of the American College of Cardiology and the American Heart Association (ACC/AHA). The main difference between them is that the JNC-7 guideline consists of three categories whereas the ACC/AHA guideline comprises four stages, leading to a change in the cut-off points for defining hypertension and the corresponding disease prevalence. Based on data from the PERU MIGRANT study, the authors found that there is evidence for an association between the different definitions of hypertension and mortality while accounting for potential confounders. This finding is in line with the results from previous studies, but has the advantage that it is based on a large population-based cohort study. As a main conclusion the authors recommend that the choice of cut-off values for defining hypertension should be balanced with respect to the patient's benefit and the capacities of the health system because of a rising number of people with hypertension using the ACC/AHA guideline.In the introduction, it was written that "prevalence of hypertension was estimated to be 24.1% and 20.1% in men and women, respectively". It would be helpful to add the year that corresponds to these prevalence's.In the introduction, we would suggest to briefly discuss the reasons that initially led to a reformulation of the definition of hypertension, i.e. what were the motives of the ACC/AHA to change the cut-off levels, and why did they introduce four instead of three categories?In the "Settings and participants" section, it remains unclear what is meant by an "age and stratified sampling approach" which is used to select participants of the PERU MIGRANT study. This should be clarified.What is the reason for splitting age into two categories (< 50 vs. >= 50)? With respect to the power of the analysis, it would be far better to include age as a covariate measured on a continuous scale. We were also wondering about the reason for the age cut-off at year 30? Is there a reason why no children, adolescents and any other people below 30 years are excluded?We would strongly recommend to delete all p-values from tables 1-3 and to purely report on the characteristics of the population. This would be in line with current guidelines used to report the results of observational studies (e.g. STROBE).We are a bit confused about the term "Kaplan-Meier test" and would suggest that the authors explain in more detail what specific kind of statistical test they used. If they are interested in the association between the exposure and outcome, it would be also possible to assess the estimated hazard ratios (HR) and their confidence intervals based on the proportional hazard regression approach.Drawing conclusions about potential associations between variables based on univariate p-values (tables 1-3) alone should be viewed critically because the PERU MIGRANT study is a cohort study. That means that there are indeed confounding variables that are not adjusted for in tables 1-3. Again, we would recommend to delete all p-values from tables 1-3, to simply describe the characteristics of the study participants and to avoid statements about potential associations based on single p-values.The term "bivariate model" should be avoided when only the association between two variables is meant because in a bivariate analysis, the outcome consists of two variables.With respect to the Cox model, we would suggest to interpret the HR in terms of hazards and not risks. That means, a HR of 5.1 indicates a 5.1-fold increased hazard (and not a risk). It should also be added compared to which group the hazard is increased (or decreased).We were wondering if the authors took account for possible left truncation which maybe occurred because the minimum age of entry is 30 years.When discussing the AIC and BIC for the models, the authors should be more precise and state which models were compared and which result refers to which comparison.In the section where the authors compare their findings with other studies, the dates of the studies mentioned should be added.In the introduction and conclusion, the authors highlight that three different population groups were separately considered. Though, in the result section this is not further taken up. We would recommend that the authors explain why the distinction is yet so important and if the findings of their study differ with regards to the different groups.From our point of view, the conclusion that 46% of deaths could be avoided using the JNC-7 definition, and 66.7% using the ACC/AHA 2017 guideline if hypertension is treated, seem a bit speculative. Could deaths be really prevented if people are classified as being hypertensive? We think such a conclusion cannot be drawn from a cohort study and the analysis done by the authors because it is not known if hypertension was the reason for all reported deaths. Only associations are seen. This should be discussed in more detail. We would also suggest to weaken this conclusion.We would suggest that the authors explain the implications of their study in more detail. As a concluding remark, they have written that their estimates can be used to inform local decision making. We were wondering how this can be achieved based on the estimated HRs because they do not differ that much between the two definitions of hypertension. It would be helpful to describe in which way these estimates can be used for decision making with respect to benefits and risks for patients with newly diagnosed hypertension. Minor comments:The authors may want to add a decimal separator .With regards to abbreviations: They should be introduced when first used (e.g. page 9 "CV risk scores"), and only introduced when needed (e.g. STEPs is never referred to again). First, the full name should be spelled and then the abbreviation should be added in brackets ). Abbreviations in the abstract should be avoided (e.g. HR).The authors should be consistent in their writing and with the vocabulary used in the tables. E.g. "stage 1" vs. "stage-1" vs. "stage I" (same applies for “stage 2”) and "Las Pampas de San Juan de Miraflores" vs. "San Juan de Miraflores".Abstract: The data of the PERU MIGRANT Study was used, not the analysis, we think.Page 3, Settings and Participants: The sentence “In Lima, a highly urbanized city, Las Pampas de San Juan de Miraflores was selected as the urban environment,” should be rephrased as follows “Las Pampas de San Juan de Miraflores, a highly urbanized city in Lima, Peru, was selected as the urban environment....”.Page 4: There is a typo in the Procedures section. It should be read “measured” instead of “measure” in the following sentence: “Blood pressure was measured in seated position after a resting period of five minutes.”Page 4: Another typo is found in the Ethics section. Instead of “in UK” the authors should write “in the UK” at the end of the first sentence of the mentioned paragraph.(please add a reference) …”.Page 7: When discussing the main findings, we would suggest reformulating the following sentence on page 7 to “High blood pressure levels have been found to increase the risk of 10-year all-cause mortality The article is well written and spreads light on the association between blood pressure and mortality depending on the cut-off for defining high blood pressure in the Peruian population. However, we have some comments and suggestions that may improve the article:Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Biostatistics, Epidemiology, Diabetes, Chronic DiseasesI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. REVIEWER 1 - ANNIKA HOYER The article is well written and spreads light on the association between blood pressure and mortality depending on the cut-off for defining high blood pressure in the Peruvian population. However, we have some comments and suggestions that may improve the article:In the introduction, it was written that "prevalence of hypertension was estimated to be 24.1% and 20.1% in men and women, respectively". It would be helpful to add the year that corresponds to these prevalence's.Response: We have rewritten the sentence to clarify this point. Now, it reads: “…and the prevalence of hypertension in 2015 was estimated to be 24.1% and 20.1% in men and women, respectively.”In the introduction, we would suggest to briefly discuss the reasons that initially led to a reformulation of the definition of hypertension, i.e., what were the motives of the ACC/AHA to change the cut-off levels, and why did they introduce four instead of three categories?Response: Although the focus of the paper was not to define the motivations of the ACC/AHA to change the cut-off levels, we have introduced some lines regarding this point. Now, it reads: “In 2017, as part of the ongoing review process of full guidelines commissioned in about 6-year cycles, the American College of Cardiology and the American Heart Association (ACC/AHA 2017) changed the proposed cut-off points used for defining hypertension, …”In the "Settings and participants" section, it remains unclear what is meant by an "age and stratified sampling approach" which is used to select participants of the PERU MIGRANT study. This should be clarified.Response: Thanks for noticing these typos. We have corrected this section to better clarify this point. Now, it reads: “Participants were randomly selected using an age-  and sex- stratified sampling approach, utilizing the most up-to-date census in the study area.”What is the reason for splitting age into two categories (< 50 vs. >= 50)? With respect to the power of the analysis, it would be far better to include age as a covariate measured on a continuous scale.Response: Age is considered as confounder in our models. However, we have changed the categorization of age in four categories based on the stratification of the sample . We have accordingly modified paper results. Overall, HR reduced after controlling for age in 4 categories, but are yet consistent.We were also wondering about the reason for the age cut-off at year 30? Is there a reason why no children, adolescents and any other people below 30 years are excluded?Response: The original study at baseline recruited participants aged 30 years and over, and this was done because cardiovascular conditions are most common among subjects aged ≥ 30 years. This is a secondary analysis of that data and for instance no data of other age group was available.We would strongly recommend to delete all p-values from tables 1-3 and to purely report on the characteristics of the population. This would be in line with current guidelines used to report the results of observational studies .Response: We have decided to keep the p-values in Tables 1 to 3 as they are important to show association of variables with the exposures and outcome of interest, and for instance, the role of potential confounders. We ask the editor to decide about this topic if relevant.We are a bit confused about the term "Kaplan-Meier test" and would suggest that the authors explain in more detail what specific kind of statistical test they used. If they are interested in the association between the exposure and outcome, it would be also possible to assess the estimated hazard ratios (HR) and their confidence intervals based on the proportional hazard regression approach.Response: We have rewritten this section to clarify this point. It now reads: “A plot of the Kaplan-Meier estimator was used to evaluate the assumption of proportional hazards graphically, whereas such assessment was done in post-hoc analysis using the Schoenfeld residuals.”Drawing conclusions about potential associations between variables based on univariate p-values (tables 1-3) alone should be viewed critically because the PERU MIGRANT study is a cohort study. That means that there are indeed confounding variables that are not adjusted for in tables 1-3. Again, we would recommend to delete all p-values from tables 1-3, to simply describe the characteristics of the study participants and to avoid statements about potential associations based on single p-values.Response: We only described the findings in our Results section. To avoid confusion, we have rewritten this section as follows: “In both definitions, high blood pressure levels were more frequent among males, older subjects, migrant and urban dwellers, as well as those with obesity and those with type 2 diabetes mellitus”.The term "bivariate model" should be avoided when only the association between two variables is meant because in a bivariate analysis, the outcome consists of two variables.Response: We have changed the term by using “bivariable model”.With respect to the Cox model, we would suggest to interpret the HR in terms of hazards and not risks. That means, a HR of 5.1 indicates a 5.1-fold increased hazard (and not a risk). It should also be added compared to which group the hazard is increased (or decreased).Response: We have rewritten this section as suggested: “Using the JNC-7 guideline and compared to those with normal blood pressure, those with pre-hypertension and hypertension had 2-fold and 3.4-fold increased hazard of death, respectively. On the other hand, using the ACC/AHA 2017 definition and compared to those with normal blood pressure, stage 1 and stage 2 hypertension were associated with a 2.5- and 3.5-fold increase in the hazard of mortality.”We were wondering if the authors took account for possible left truncation which maybe occurred because the minimum age of entry is 30 years.Response: Although left truncation can be present as minimum age at study entry was 30 years, we believe the effect can be negligible as the prevalence of hypertension in Peruvian population aged <30 years is very low (<3%). However, we have added that in the limitations section: “Third, although left truncation can be present as subjects <30 years were excluded, the potential effect of such limitation may be negligible as the prevalence of hypertension is low in that age group.”When discussing the AIC and BIC for the models, the authors should be more precise and state which models were compared and which result refers to which comparison.Response: We have clarified this point in the Statistical analysis section: “Using full-adjusted models, Akaike and Bayesian information criteria (AIC and BIC) as well as the Nelson-Aalen graphs were utilized to compare both blood pressure level definitions and their impact on mortality.” In addition, this has been also clarified in the Results section: “When comparing adjusted models, AIC and BIC were very similar , highlighting no difference between models.”In the section where the authors compare their findings with other studies, the dates of the studies mentioned should be added.Response: We have modified this section accordingly. We have added dates of recruitment of the studies.In the introduction and conclusion, the authors highlight that three different population groups were separately considered. Though, in the result section this is not further taken up. We would recommend that the authors explain why the distinction is yet so important and if the findings of their study differ with regards to the different groups.Response: We have not included population groups in the Introduction section. However, we have included such information in the Methods section as it is needed to describe the cohort used in the analysis, and in the Discussion section, especially in the Strength and Limitations section. We have dropped this section as it does not affect the interpretation of the results.From our point of view, the conclusion that 46% of deaths could be avoided using the JNC-7 definition, and 66.7% using the ACC/AHA 2017 guideline if hypertension is treated, seem a bit speculative. Could deaths be really prevented if people are classified as being hypertensive? We think such a conclusion cannot be drawn from a cohort study and the analysis done by the authors because it is not known if hypertension was the reason for all reported deaths. Only associations are seen. This should be discussed in more detail. We would also suggest to weaken this conclusion.Response: This section highlighted by the reviewer is not a conclusion, it is only a finding described taking into account the limitations of the study . However, based on the comment, we have deleted this section.We would suggest that the authors explain the implications of their study in more detail. As a concluding remark, they have written that their estimates can be used to inform local decision making. We were wondering how this can be achieved based on the estimated HRs because they do not differ that much between the two definitions of hypertension. It would be helpful to describe in which way these estimates can be used for decision making with respect to benefits and risks for patients with newly diagnosed hypertension.Response: We believe all these topics are discussed in the Public Health Relevance section: (1) the increase in the prevalence of hypertension, and the use of medications would start at a lower blood pressure level, with the subsequent increase in care costs at the health system which will be translated to the patient. So, countries like Peru are not prepared for that change, and for instance, it will require appropriate strategies to face them. In addition, we also discuss in this section the increase of side effects due to the greater use of anti-hypertensive medication.The authors may want to add a decimal separator .Response: This will depend on the journal style and not necessarily on us. The paper has been adapted for review by editors, so we believe this may be not important at all.With regards to abbreviations: They should be introduced when first used (e.g. page 9 "CV risk scores"), and only introduced when needed (e.g. STEPs is never referred to again). First, the full name should be spelled and then the abbreviation should be added in brackets ). Abbreviations in the abstract should be avoided .Response: The meaning of SPRINT Study is in the introduction of the paper. The use of abbreviations in the abstract depends on the journal style. We have nevertheless described the meaning of such abbreviations.The authors should be consistent in their writing and with the vocabulary used in the tables. E.g. "stage 1" vs. "stage-1" vs. "stage I" (same applies for “stage 2”)and "Las Pampas de San Juan de Miraflores" vs. "San Juan de Miraflores".Response: We have modified the manuscript to be consistent with our terms.Abstract: The data of the PERU MIGRANT Study was used, not the analysis, we think.Response: We have modified this section.Page 3, Settings and Participants: The sentence “In Lima, a highly urbanized city, Las Pampas de San Juan de Miraflores was selected as the urban environment,” should be rephrased as follows “Las Pampas de San Juan de Miraflores, a highly urbanized city in Lima, Peru, was selected as the urban environment....”.Response: We have modified this section as suggested.Page 4: There is a typo in the Procedures section. It should be read “measured” instead of “measure” in the following sentence: “Blood pressure was measured in seated position after a resting period of five minutes.”Response: We have modified this section as suggested.Page 4: Another typo is found in the Ethics section. Instead of “in UK” the authors should write “in the UK” at the end of the first sentence of the mentioned paragraph.Response: We have modified this section as suggested.Page 7: When discussing the main findings, we would suggest reformulating the following sentence on page 7 to “High blood pressure levels have been found to increase the risk of 10-year all-cause mortality (please add a reference) …”.Response: This sentence is not part of a reference but instead the interpretation of our findings. We have clarified that by adding an introduction phase as follows: “According to our findings, high blood pressure levels increased the risk of 10-year all-cause mortality, …” The article is well written and spreads light on the association between blood pressure and mortality depending on the cut-off for defining high blood pressure in the Peruvian population. However, we have some comments and suggestions that may improve the article:In the introduction, it was written that "prevalence of hypertension was estimated to be 24.1% and 20.1% in men and women, respectively". It would be helpful to add the year that corresponds to these prevalence's.Response: We have rewritten the sentence to clarify this point. Now, it reads: “…and the prevalence of hypertension in 2015 was estimated to be 24.1% and 20.1% in men and women, respectively.”In the introduction, we would suggest to briefly discuss the reasons that initially led to a reformulation of the definition of hypertension, i.e., what were the motives of the ACC/AHA to change the cut-off levels, and why did they introduce four instead of three categories?Response: Although the focus of the paper was not to define the motivations of the ACC/AHA to change the cut-off levels, we have introduced some lines regarding this point. Now, it reads: “In 2017, as part of the ongoing review process of full guidelines commissioned in about 6-year cycles, the American College of Cardiology and the American Heart Association (ACC/AHA 2017) changed the proposed cut-off points used for defining hypertension, …”In the "Settings and participants" section, it remains unclear what is meant by an "age and stratified sampling approach" which is used to select participants of the PERU MIGRANT study. This should be clarified.Response: Thanks for notice these typos. We have corrected this section to better clarify this point. Now, it reads: “Participants were randomly selected using an age-  and sex- stratified sampling approach, utilizing the most up-to-date census in the study area.”What is the reason for splitting age into two categories (< 50 vs. >= 50)? With respect to the power of the analysis, it would be far better to include age as a covariate measured on a continuous scale.Response: Age is considered as confounder in our models. However, we have changed the categorization of age in four categories based on the stratification of the sample . We have accordingly modified paper results. Overall, HR reduced after controlling for age in 4 categories, but are yet consistent.We were also wondering about the reason for the age cut-off at year 30? Is there a reason why no children, adolescents and any other people below 30 years are excluded?Response: The original study at baseline recruited participants aged 30 years and over, and this was done because cardiovascular conditions are most common among subjects aged ≥ 30 years. This is a secondary analysis of that data and for instance no data of other age group was available.We would strongly recommend to delete all p-values from tables 1-3 and to purely report on the characteristics of the population. This would be in line with current guidelines used to report the results of observational studies .Response: We have decided to keep the p-values in Tables 1 to 3 as they are important to show association of variables with the exposures and outcome of interest, and for instance, the role of potential confounders. We ask the editor to decide about this topic if relevant.We are a bit confused about the term "Kaplan-Meier test" and would suggest that the authors explain in more detail what specific kind of statistical test they used. If they are interested in the association between the exposure and outcome, it would be also possible to assess the estimated hazard ratios (HR) and their confidence intervals based on the proportional hazard regression approach.Response: We have rewritten this section to clarify this point. It now reads: “A plot of the Kaplan-Meier estimator was used to evaluate the assumption of proportional hazards graphically, whereas such assessment was done in post-hoc analysis using the Schoenfeld residuals.”Drawing conclusions about potential associations between variables based on univariate p-values (tables 1-3) alone should be viewed critically because the PERU MIGRANT study is a cohort study. That means that there are indeed confounding variables that are not adjusted for in tables 1-3. Again, we would recommend to delete all p-values from tables 1-3, to simply describe the characteristics of the study participants and to avoid statements about potential associations based on single p-values.Response: We only described the findings in our Results section. To avoid confusion, we have rewritten this section as follows: “In both definitions, high blood pressure levels were more frequent among males, older subjects, migrant and urban dwellers, as well as those with obesity and those with type 2 diabetes mellitus”.The term "bivariate model" should be avoided when only the association between two variables is meant because in a bivariate analysis, the outcome consists of two variables.Response: We have changed the term by using “bivariable model”.With respect to the Cox model, we would suggest to interpret the HR in terms of hazards and not risks. That means, a HR of 5.1 indicates a 5.1-fold increased hazard (and not a risk). It should also be added compared to which group the hazard is increased (or decreased).Response: We have rewritten this section as suggested: “Using the JNC-7 guideline and compared to those with normal blood pressure, those with pre-hypertension and hypertension had 2-fold and 3.4-fold increased hazard of death, respectively. On the other hand, using the ACC/AHA 2017 definition and compared to those with normal blood pressure, stage 1 and stage 2 hypertension were associated with a 2.5- and 3.5-fold increase in the hazard of mortality.”We were wondering if the authors took account for possible left truncation which maybe occurred because the minimum age of entry is 30 years.Response: Although left truncation can be present as minimum age at study entry was 30 years, we believe the effect can be negligible as the prevalence of hypertension in Peruvian population aged <30 years is very low (<3%). However, we have added that in the limitations section: “Third, although left truncation can be present as subjects <30 years were excluded, the potential effect of such limitation may be negligible as the prevalence of hypertension is low in that age group.”When discussing the AIC and BIC for the models, the authors should be more precise and state which models were compared and which result refers to which comparison.Response: We have clarified this point in the Statistical analysis section: “Using full-adjusted models, Akaike and Bayesian information criteria (AIC and BIC) as well as the Nelson-Aalen graphs were utilized to compare both blood pressure level definitions and their impact on mortality.” In addition, this has been also clarified in the Results section: “When comparing adjusted models, AIC and BIC were very similar , highlighting no difference between models.”In the section where the authors compare their findings with other studies, the dates of the studies mentioned should be added.Response: We have modified this section accordingly. We have added dates of recruitment of the studies.In the introduction and conclusion, the authors highlight that three different population groups were separately considered. Though, in the result section this is not further taken up. We would recommend that the authors explain why the distinction is yet so important and if the findings of their study differ with regards to the different groups.Response: We have not included population groups in the Introduction section. However, we have included such information in the Methods section as it is needed to describe the cohort used in the analysis, and in the Discussion section, especially in the Strength and Limitations section. We have dropped this section as it does not affect the interpretation of the results.From our point of view, the conclusion that 46% of deaths could be avoided using the JNC-7 definition, and 66.7% using the ACC/AHA 2017 guideline if hypertension is treated, seem a bit speculative. Could deaths be really prevented if people are classified as being hypertensive? We think such a conclusion cannot be drawn from a cohort study and the analysis done by the authors because it is not known if hypertension was the reason for all reported deaths. Only associations are seen. This should be discussed in more detail. We would also suggest to weaken this conclusion.Response: This section highlighted by the reviewer is not a conclusion, it is only a finding described taking into account the limitations of the study . However, based on the comment, we have deleted this section.We would suggest that the authors explain the implications of their study in more detail. As a concluding remark, they have written that their estimates can be used to inform local decision making. We were wondering how this can be achieved based on the estimated HRs because they do not differ that much between the two definitions of hypertension. It would be helpful to describe in which way these estimates can be used for decision making with respect to benefits and risks for patients with newly diagnosed hypertension.Response: We believe all these topics are discussed in the Public Health Relevance section: (1) the increase in the prevalence of hypertension, and the use of medications would start at a lower blood pressure level, with the subsequent increase in care costs at the health system which will be translated to the patient. So, countries like Peru are not prepared for that change, and for instance, it will require appropriate strategies to face them. In addition, we also discuss in this section the increase of side effects due to the greater use of anti-hypertensive medication.The authors may want to add a decimal separator .Response: This will depend on the journal style and not necessarily on us. The paper has been adapted for review by editors, so we believe this may be not important at all.With regards to abbreviations: They should be introduced when first used (e.g. page 9 "CV risk scores"), and only introduced when needed (e.g. STEPs is never referred to again). First, the full name should be spelled and then the abbreviation should be added in brackets ). Abbreviations in the abstract should be avoided .Response: The meaning of SPRINT Study is in the introduction of the paper. The use of abbreviations in the abstract depends on the journal style. We have nevertheless described the meaning of such abbreviations.The authors should be consistent in their writing and with the vocabulary used in the tables. E.g. "stage 1" vs. "stage-1" vs. "stage I" (same applies for “stage 2”)and "Las Pampas de San Juan de Miraflores" vs. "San Juan de Miraflores".Response: We have modified the manuscript to be consistent with our terms.Abstract: The data of the PERU MIGRANT Study was used, not the analysis, we think.Response: We have modified this section.Page 3, Settings and Participants: The sentence “In Lima, a highly urbanized city, Las Pampas de San Juan de Miraflores was selected as the urban environment,” should be rephrased as follows “Las Pampas de San Juan de Miraflores, a highly urbanized city in Lima, Peru, was selected as the urban environment....”.Response: We have modified this section as suggested.Page 4: There is a typo in the Procedures section. It should be read “measured” instead of “measure” in the following sentence: “Blood pressure was measured in seated position after a resting period of five minutes.”Response: We have modified this section as suggested.Page 4: Another typo is found in the Ethics section. Instead of “in UK” the authors should write “in the UK” at the end of the first sentence of the mentioned paragraph.Response: We have modified this section as suggested.Page 7: When discussing the main findings, we would suggest reformulating the following sentence on page 7 to “High blood pressure levels have been found to increase the risk of 10-year all-cause mortality (please add a reference) …”.Response: This sentence is not part of a reference but instead the interpretation of our findings. We have clarified that by adding an introduction phase as follows: “According to our findings, high blood pressure levels increased the risk of 10-year all-cause mortality, …”


Primary sclerosing cholangitis (PSC) patients have a risk of developing cholangiocarcinoma (CCA). Establishing predictive models for CCA in PSC is important.In a large cohort of 1,459 PSC patients seen at Mayo Clinic (1993–2020), we quantified the impact of clinical/laboratory variables on CCA development using univariate and multivariate Cox models and predicted CCA using statistical and artificial intelligence (AI) approaches. We explored plasma bile acid (BA) levels’ predictive power of CCA .p < 0.05) with multivariate analysis. Clinical/laboratory variables predicted CCA with cross-validated C-indexes of 0.68–0.71 at different time points of disease, significantly better compared to commonly used PSC risk scores. Lower chenodeoxycholic acid, higher conjugated fraction of lithocholic acid and hyodeoxycholic acid, and higher ratio of cholic acid to chenodeoxycholic acid were predictive of CCA. BAs predicted CCA with a cross-validated C-index of 0.66 , similar to clinical/laboratory variables . Combining BAs with clinical/laboratory variables leads to the best average C-index of 0.67 .Eight significant risk factors  were identified with univariate analysis; prolonged inflammatory bowel disease (IBD) was the most important one. IBD duration, PSC duration, and total bilirubin remained significant (In a large PSC cohort, we identified clinical and laboratory risk factors for CCA development and demonstrated the first AI based predictive models that performed significantly better than commonly used PSC risk scores. More predictive data modalities are needed for clinical adoption of these models.The online version contains supplementary material available at 10.1186/s12876-023-02759-7. Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease characterized by inflammation and fibrosis of the bile ducts, often progressing to end-stage liver disease requiring liver transplantation (LT). Nearly 75%–80% of patients with PSC have comorbid inflammatory bowel disease (IBD)  and are Prior studies have identified risk factors for CCA in PSC patients including advanced age, male sex, and prolonged duration of IBD –9. HowevPersonalized risk prediction models, such as the Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) , are curIn this study, we leverage one of the largest single-center well-phenotyped PSC cohorts to better understand the clinical/laboratory and plasma BA signatures and identify risk factors for CCA development in PSC. Using a rigorous analytical approach employing statistical and artificial intelligence (AI) approaches, we aim to narrow the gap in individualized treatment of PSC through personalized CCA risk prediction models. We expect our approach to be adaptable for future studies focusing on comprehensive multi-omics profiling of PSC patients, with the goal of providing better clinical management tools for PSC through omics based individual risk-informed surveillance programs.The cohort of patients with PSC in this study are enrolled in the PSC Scientific Community Resource . A detaiCCA was diagnosed by: (a) imaging/cholangiographic findings characteristic of CCA with positive cytology or histopathology; (b) malignant-appearing strictures with FISH polysomy plus suspicious cytology; (c) malignant-appearing strictures with FISH polysomy plus elevated serum CA 19–9; (d) malignant-appearing strictures with FISH polysomy; and (e) malignant-appearing strictures with persistently elevated serum CA 19–9 –16. Occup-value estimates from the pooled results using the predictive mean matching method in the Multivariate Imputation by Chained Equations (MICE) package [Clinical variables and laboratory parameters were abstracted from the electronic medical record (EMR) of PSC patients and used in analysis of the baseline cohort. A subset of these patients had plasma BA data available from a previous study . This co package   availabWe analyzed the incidence of CCA in patients with PSC treating GBC, HCC, LT, and death from causes other than PSC as competing events. Patients who did not develop CCA or any of the competing events were censored at the last known clinical encounter. We used the mstate package  to generWe used Cox proportional hazards models (CoxPH)  to identp-values and HRs with the 95% confidence interval (CI). The Benjamini–Hochberg procedure [We first constructed univariate CoxPH models for each baseline clinical variable and laboratory parameter and reported their rocedure  was perfWe first constructed a set of models using baseline clinical variables and laboratory parameters of the baseline cohort. Censoring was again made at the time of GBC, HCC, LT, death, or the last clinical encounter, whichever occurred first. Patients without an IBD diagnosis were considered to have an IBD duration of 0 year. We similarly excluded the composite scores and direct bilirubin. We excluded total IgG and IgG4 for their high missingness. We applied Yeo-Johnson power transformation  to the hThree models were used in predicting CCA: (i) the abovementioned multivariate CoxPH model with regularization term(s); (ii) Random Survival Forest (RSF) , and . C-index values range from 0 to 1. A high C-Index indicates the model correctly predicts higher risk for patients who developed CCA in shorter times. We calculated the mean and standard deviation of the test set C-index from a 20-fold Monte Carlo cross-validation with 80%–20% train–test split. Specifically, we randomly split the dataset into a training set (80%) and a test set (20%) and repeated this process 20 times. The results were calculated by taking the mean and standard deviation of the test set C-index across all 20 splits. All models shared the same train–test splits to ensure a fair comparison.We calculated the permutation feature importance by measuring decrease in C-index of each model when the values of a feature were randomly permuted (across test set patients). For each cross-validation fold, the permutation was repeated three times, and the mean decrease in the test set C-index was evaluated. To assess how well the model performs at different time points in the disease course, we also evaluated the models using clinical variables and laboratory parameters collected at 2 and 5 years post PSC diagnosis. Time related clinical variables such as age and disease duration were updated according to the status at the evaluated time points.For the BA cohort, we constructed a second set of models using plasma BAs and/or clinical variables and laboratory parameters. Composite BA variables with no blank values were also included. We employed the same methodology as above, except due to the smaller number of patients and CCA occurrences in the BA cohort, we adopted a more balanced 70%–30% train–test split and performed recursive feature elimination on the training set to select the three most important BAs associated with CCA. In CoxPH, since feature selection was explicitly performed, we dropped the l1 regularization term and used only an l2 regularization term with 0.01 as the parameter. We kept the choices of other hyperparameters consistent with the models for the baseline cohort. The models were then trained with the selected BAs, and C-index was calculated on the test set. To compare the predictive power of BAs, clinical variables, and laboratory parameters, we repeated the abovementioned procedure replacing BAs with clinical/laboratory variables. Furthermore, we trained models combining the three selected BAs and the three selected clinical/laboratory variables from the corresponding cross-validation fold. Feature importance was again measured with three permutations.A total of 1,459 PSC patients were included in the baseline cohort, and their characteristics are summarized in Table Of the 118 PSC patients who developed CCA prior to any other outcomes, the diagnosis of CCA was established/confirmed pretreatment and/or posttreatment histopathologically and/or cytologically in 78.8% (93/118). To elaborate, pre-CCA treatment cytology positive for adenocarcinoma was identified in 52 patients and histopathology positive for adenocarcinoma in 20 patients. Further, histopathology on liver explant positive for adenocarcinoma was identified in 8 patients, histopathology on partial hepatectomy specimens positive for adenocarcinoma in 6 patients, fine needle aspiration of liver/metastatic mass positive for adenocarcinoma in 6 patients, and on en bloc resection of the bile ducts in 1 patient. Of the 25 patients without tissue diagnosis, 8 had clear evidence of CCA by a visible mass on abdominal cross-sectional imaging, 6 patients had malignant-appearing stricture with suspicious cytology and polysomy on FISH, 6 patients had malignant-appearing strictures with negative cytology but polysomy on FISH, and 5 patients had malignant-appearing strictures and elevated serum CA 19–9.p < 0.05).The HRs of the baseline clinical variables and laboratory parameters from the univariate CoxPH models are summarized in Table p < 0.005 comparing any of the predictive models with any of the risk scores). We evaluated performance of the models at future time points, 2 and 5 years post PSC diagnosis. The C-index values of each model at 2 and 5 years post PSC diagnosis remained unchanged compared to baseline.The test C-index values of different models in predicting CCA-free survival for the baseline cohort using clinical variables and laboratory parameters are shown in Table The permutation feature importance for the baseline clinical variables and laboratory parameters is shown in Fig. p < 0.05), although they were not statistically significant when controlling for FDR.Of the 1,459 PSC patients, 300 had BA data and constituted the BA cohort. The median time from PSC diagnosis until the last clinical encounter in this cohort was 9.6 years (IQR: 5.6–17.1), and during follow-up, 21 (7.0%) of the 300 patients were diagnosed with CCA. BA values, clinical variables and laboratory parameters of the BA cohort are provided in Table Sp = 0.036) and marginally significant for RSF (p = 0.054). Whereas, the loss in performance using BA variables for CoxPH model was not significant (p = 0.572). When selected clinical variables, laboratory parameters, and BAs were combined, the RSF and GBSA models had improved performance compared to when using only the clinical/laboratory variables alone (p < 0.01)  as endpoint, they do not consider risk factors for CCA, such as IBD duration or CA19-9, which were shown to be the most important predictors in our individualized models. Data from our study substantiate the notion that CCA in PSC presents a complex interaction of clinical, biochemical, genetic, and environmental factors that it might not be possible to identify only using the routinely obtained clinical and laboratory variables.Part of the putative complex interaction might be explained by the BAs. We hence examined plasma BA data as an additional data modality for the prediction of CCA development in PSC. Our results showed that, in the BA cohort, BAs predicted CCA with a C-index of 0.66. To put this number in context, in the same BA cohort, clinical variables and laboratory parameters predicted CCA with a C-index of 0.64, slightly worse than the BAs-based prediction. Combining selected BAs, clinical variables and laboratory parameters resulted in the best predictive performance with a C-index of 0.67. It is worth noting that the relatively small number of CCA cases available for training (15 CCA cases on average) hindered the models’ abilities to accurately learn the relationships between predictors and CCA. Furthermore, we only selected three features (six features in the combined scenario) to reduce overfitting. Since our preliminary results for the BA cohort suggest that BAs improved CCA prediction beyond clinical variables and routine laboratory parameters, it would be valuable to study the predictability of BAs in a larger patient population. A larger cohort would enable the models to accurately learn the relationships and retain richer information from a wider set of features.AI involves computer programs,which can execute functions that we associate with human intelligence, such as learning . AI techOur study has limitations. Although it provides valuable preliminary results on predicting CCA in patients with PSC and showed that individualized predictive models were significantly better than the commonly used risk scores, models with better performance are needed for direct clinical utility. We believe the present work sets the stage for future efforts aimed at development of more accurate CCA risk determination tools, which are desperately needed in clinical practice. The use of cross-sectional data limited our ability to comment on the importance of different risk factors over time, this could be addressed with longitudinal data. The patients in this study were largely seen in academic tertiary high-volume medical centers, and thus are more likely to be inherently complex cases. Furthermore, the frequency of CCA cases among our PSC population is higher compared to other medical centers due to the fact that our institution is a referral center for these diseases. Consequently, the results of our study may not necessarily apply to the entire PSC population. It should be noted that the clinical applicability of our results requires cross-validation in an independent PSC cohort. The criteria we use for CCA diagnosis may be different from other medical centers. The diagnosis of CCA can be extremely challenging especially in patients with PSC due to its silent clinical presentation and lack of accurate and sensitive markers. It requires high clinical suspicion combined with comprehensive laboratory, imaging, and endoscopic evaluation. Positive cytology, although 100% specific for CCA, has very low sensitivity, as low as 20% . Thus, gIn a large well-documented PSC cohort, we identified clinical and laboratory risk factors for CCA development and examined a statistical learning method and two AI methods that predicted CCA occurrence significantly better than common risk scores. We explored the use of BAs as novel biomarekers, which showed promise for improving CCA prediction. Larger studies and novel biomarker studies are needed for clinical adoption of these models to improve the care of these patients.Additional file 1: Fig. S1. Derivation of baseline cohort and bile acid cohort. Baseline cohort includes 1,459 patients who had pre-outcome laboratory parameters following PSC diagnosis, out of which 118 developed CCA prior to any other outcomes. Bile acid cohort includes 300 patients with pre-outcome plasma bile acids and laboratory parameters collected at similar times, out of which21 developed CCA prior to any other outcomes. Abbreviations: ALK, alkaline phosphatase; AST, alanine aminotransferase; AST, aspartate aminotransferase; CA19-9, carbohydrate antigen 19-9; CCA, cholangiocarcinoma; GBC, gallbladder cancer; IBD, inflammatory bowel disease; HCC, hepatocellular carcinoma; IgG,immunoglobulin; IgG, immunoglobulin G4; INR, international normalized ratio;LT, liver transplantation; MELD, model for end-stage liver disease; PSC,primary sclerosing cholangitis; PREsTo, PSC Risk Estimation Tool. Please see separate list of abbreviations for bile acids. Fig. S2. Cumulative incidence function (CIF) of CCA based on a competing risks framework with GBC,HCC, LT and non-PSC death as competing risks. Patients without any events were censored at the last known clinical encounter. CIF of CCA represents the cumulative probability of developing CCA (without developing any other events).Abbreviations: CCA, cholangiocarcinoma; GBC, gallbladder cancer; HCC,hepatocellular carcinoma; LT, liver transplantation; PSC, primary sclerosing cholangitis. Table S1. Summary characteristics of the bile acid cohort with median (IQR) listed for the continuous features and percentage listed for the binary features.


Background: Chronic heart failure (HF) is one of the conditions commonly seen in the medical outpatient departments, and iron deficiency (ID) has been reported as the commonest nutritional deficiency in these patients. The presence of ID may interfere with the clinical parameters of chronic HF. The relationship between iron status and chronic HF needs more attention and should be given more consideration in the evaluation of patients with chronic HF.Aim: The aim of the study was to determine the relationship, if any, between iron status and clinical/echocardiographic variables in chronic HF.Methods and materials: A cross-sectional descriptive study was carried out at the Lagos University Teaching Hospital (LUTH), Nigeria, where 88 patients with chronic HF were recruited to participate in this study. The participants underwent clinical and laboratory assessments. Iron status was assessed with full blood count parameters; serum ferritin and transferrin saturation (Tsat) and its relationship with clinical parameters among these participants were also studied.Results: No correlations existed between the duration of chronic HF and iron status when compared using Tsat. However, a significant weak negative correlation was observed between the duration of HF and the serum ferritin levels. The clinical characteristics of the HF participants with and without ID were compared. There was no significant difference in the frequency of prior hospitalization in both groups. However, a higher proportion of participants with severe HF (New York Heart Association (NYHA) classes III/IV)  were iron-deficient compared to those with moderate chronic HF (NYHA II) . This relationship was statistically significant. Left ventricular ejection fraction (LVEF) was similar in the iron-deficient and iron-replete groups (using serum ferritin or Tsat) both when compared as means and when compared after categorizing LVEF as HF with preserved ejection fraction (HFpEF) vs HF with reduced ejection fraction (HFrEF). There was no statistically significant correlation between the severity of ID and LVEF. Conclusion: A spectrum of clinical changes occurs in patients with chronic HF. ID can make these changes more profound and the condition less amenable to standard HF treatments. These patients may therefore benefit from further evaluation for this nutritional deficiency. Laboratory measurements including Tsat and serum ferritin may help in further assessment of select patients with worse and/or non-responsive clinical parameters. Heart failure (HF) has a myriad of causes, and its management may be quite challenging even to an astute clinician. Among the general adult population, the frequency of HF is said to be 1-2% . These pHF is a clinical syndrome characterized by typical symptoms including breathlessness, ankle swelling, and fatigue that may be accompanied by signs such as elevated jugular venous pressure, pulmonary crackles, and peripheral oedema and caused by a structural and/or functional cardiac abnormality, resulting in a reduced cardiac output and/or elevated intracardiac pressures at rest or during stress . When haIron is one of the first five important micronutrients along with zinc, folate, iodine, and vitamin A . Iron isThe human body is highly efficient in conserving its iron stores although minimal losses occur daily from the shedding of intestinal epithelial cells, skin, and appendage cells. However, considerable losses can also occur from monthly blood loss in females from menstruation ,19. IronID may co-exist in patients with chronic HF, but little has been done to determine its presence in Black patients with chronic HF in the Sub-Saharan environment. Also, there is a paucity of data showing the relationship between the clinical parameters identified in chronic HF and the iron status of these patients in our African setting. This study evaluated the iron status in the participants and determined the clinical and echocardiographic correlates of ID in chronic HF, where present. The information from this study may influence management protocols, which would potentially include evaluation of iron status in chronic HF patients and iron treatment in those with ID to improve outcomes in the Sub-Saharan environment.This was a cross-sectional descriptive study carried out at the Lagos University Teaching Hospital (LUTH), a tertiary hospital located in Idi-Araba, Surulere, Lagos State, Nigeria. Known adult patients visiting cardiology outpatient clinics with chronic HF were recruited for this study. = (Z2pq)/d2) [Applying the formula for cross-sectional studies for populations less than 10,000 (n2pq)/d2)  while us2pq)/d2) , a samplInterviewer-administered questionnaires were used to obtain patients' sociodemographic data and anthropometric measurements, including weight and height. Blood pressure data were also obtained from these patients. Haematological parameters assessed among the participants included Hb concentration, serum ferritin, serum transferrin, and peripheral blood film features.Ethical permission was granted by the Lagos University Teaching Hospital Health Research Ethics Committee .Data generated from the clinical, biochemical, and haematological parameters were analyzed using the IBM SPSS Statistics for Windows, Version 20 . Normally distributed numerical data were presented as means and standard deviations, while nonparametric data were expressed as medians and interquartile ranges. Categorical variables were presented as proportions. Means were compared using Student's t-test. Chi-square statistics were used to compare proportions between groups. Regression analysis was used to demonstrate the association between iron status and clinical variables. P values less than 0.05 were considered significant.Categorical variables were compared using the chi-square test or Fisher’s exact test when cell counts were less than 5. The medians of variables with a skewed distribution were compared using the Mann-Whitney U test. Correlation analysis was performed to demonstrate the association between iron status and clinical parameters. Pearson’s correlation was performed for normally distributed variables, while Spearman’s rank correlation was employed for variables that did not meet normality assumptions. Forward stepwise multiple linear regression analyses were performed to determine the clinical and laboratory parameters that were independently associated with Tsat and serum ferritin levels among the study population. Forward stepwise binary logistic regression analysis was also performed to determine the clinical parameters that were independently associated with iron status.Sociodemographic characteristicsThe patients recruited for this study were between 25 and 78 years of age. On average, they were 53.99 years old. More than half, or 55.7%, of the people taking part in the study were between the ages of 45 and 64, which is considered middle-aged. There were slightly more women than men, with 47 women and 41 men. The ratio of men to women was 0.9 to 1. Most of the participants in the study had a tertiary level of education (55.7%), 26.1% had a secondary level of education, and some had only finished primary school (15.9%). Only a few (2.3%) had never gone to school. The average body mass index (BMI) was 27.61±6.42 (see Table The New York Heart Association (NYHA) classification of HF is shown in Figure The prevalence of anaemia and ID is shown in Table Correlation between selected clinical parameters and iron status = -0.206, p = 0.03). No correlation was observed between serum ferritin levels and age (p = 0.10), BMI (p = 0.45), frequency of hospitalizations (p = 0.42), pulse rate (p = 0.10), systolic blood pressure (p = 0.33), diastolic blood pressure (p = 0.39) and left ventricular ejection fraction (LVEF) (r = 0.35). There was also no association between Tsat and any of the selected clinical parameters with one exception. A weak signal (p = 0.05) was seen between Tsat and pulse rate , whereas no significant correlation existed between age and Tsat in the iron-deficient group . There was no significant correlation between Tsat and BMI, duration of HF, frequency of hospitalizations, pulse rate, systolic blood pressure and diastolic blood pressure, in both the iron-deficient and iron-replete groups  and those with Tsat >20% (iron-replete group). Correlations were then sought between Tsat levels in each group and the specified clinical parameter. A statistically significant weak inverse correlation was seen between age and Tsat in the iron-replete group , and IV, 11 (36.7%), while only five (16.7%) of the ID participants were in NYHA II. This finding is similar to previous reports by researchers. Compared with those in moderate HF (NYHA II), Jankowska et al. found that the presence of ID was significantly greater in those with advanced HF (NYHA III/IV) . In the In this study, no correlations existed between the duration of HF and Tsat. However, a significant weak negative correlation was seen between the duration of HF and serum ferritin levels. This suggests that ID was more in participants with a longer duration of HF compared to those with a shorter duration. Jankowska et al. also reported duration of HF as an independent determinant of ID . This isThis study demonstrated significantly greater proportions of participants with advanced HF (NYHA III/IV) having ID compared to those with stable HF (NYHA II). The association between ID and NYHA class cannot be over-emphasized as the latter is a demonstration of the functional capacity of these participants which may bear a direct relationship with their exercise capacity. It is expected that more persons with advanced HF would have ID compared to the less severe functional class (NYHA II). Even though advanced HF has been associated with anaemia, correction of ID, regardless of Hb status, has also been shown to significantly improve the NYHA functional class ,27,28. LStudy limitationsThe true prevalence of ID in HF patients may not have been obtained, as this was a hospital-based study. Also, bone marrow iron is considered a direct test for iron stores and the most reliable to evaluate iron status. Due to its invasiveness and discomfort associated with bone marrow aspiration, it was not carried out in the participants. More so, there are indirect comparable means of measuring iron status, which are readily available and have been employed in this study.Erythrocyte ferritin is superior to serum ferritin, as it is not affected by inflammation, which is seen in HF. However, only a few machines can measure it, which are not available in our setting.There were no echocardiographic differences (using LVEF) between the ID group and participants who had no decline in their iron status. Changes in iron status were more likely to attract differences in clinical variables in chronic HF. Participants with ID showed a trend towards higher pulse rates and had advanced NYHA functional classes compared to those that were iron-replete. There was a strong association between ID and NYHA functional class: ID occurred more frequently in NYHA classes III and IV than in NYHA II. Iron studies (Tsat and serum ferritin) should be recommended for patients in NYHA III/IV who are not satisfactorily responsive to optimal heart failure therapy. Chronic HF patients with ID should be enrolled in randomized interventional trials to clarify the unmet needs of specific therapy for this patient group.