FI130199B - Method for determining whether a subject is at risk of developing a renal disease - Google Patents

Method for determining whether a subject is at risk of developing a renal disease Download PDF

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FI130199B
FI130199B FI20215180A FI20215180A FI130199B FI 130199 B FI130199 B FI 130199B FI 20215180 A FI20215180 A FI 20215180A FI 20215180 A FI20215180 A FI 20215180A FI 130199 B FI130199 B FI 130199B
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fatty acids
disease
renal
icd
risk
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FI20215180A1 (en
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Peter Würtz
Heli Julkunen
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Nightingale Health Oy
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    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
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    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
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    • G01N2800/50—Determining the risk of developing a disease

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Abstract

A method for determining whether a subject is at risk of developing a renal disease is disclosed.

Description

METHOD FOR DETERMINING WHETHER A SUBJECT IS AT RISK OF
DEVELOPING A RENAL DISEASE
TECHNICAL FIELD
The present disclosure relates generally to methods for determining whether a subject is at risk of developing a renal disease.
BACKGROUND
Renal diseases comprise various conditions re- lated to the kidneys. Common examples of such diseases include, for instance, renal cancer, glomerular dis- eases, tubulo-interstitial diseases, acute kidney fail- ure, chronic kidney disease and urolithiasis. Renal con- ditions often go unnoticed until late stages of the disease when the symptoms become severe. Symptoms of advanced renal diseases often include fatigue, swollen ankles, feet or hands (due to water retention), short- ness of breath, nausea and blood in the urine (haematu- ria).
Farly identification of individuals at an el- evated risk of developing a renal disease may be im- portant for early treatment and to prevent the develop- ment of more serious conditions, such as kidney failure.
Various blood biomarkers may be useful for predicting whether an individual is at an elevated risk of devel- oping various renal conditions. As these diseases can
N have serious consequences if not controlled effectively
N and treated early, biomarkers predictive of the onset 3 30 of these disorders would enable more effective screen- 2 ing, earlier diagnosis and preventative treatment. Such =E biomarkers may be measured from biological samples, for + example from blood samples or related biological fluids. > Kettunen et al., Circulation: Genomic and Pre- = 35 cision Medicine 2018, 11, 11, e002234 describes the as-
S sociation of glycoprotein acetyls (GlycA) with mortality risk and incident diseases.
Titan et al., BMC Nephrology 2017, 18, 367, 1- 9 describes the evaluation of the association of GlycA with albuminuria and estimated glomerular filtration rate.
Dierckx et al., Journal of Clinical Medicine 2020, 9, 4, 1-16 investigates the relevance of serum
GlycA in systemic lupus erythematosus patients.
Muhlestein et al., American Heart Journal 2018, 202, 27-32 describes associations between GlycA and car- diovascular events.
US 2020064344 discloses the use of serum 2- cysteine peroxiredoxins as biomarkers of chronic kidney diseases.
WO 2014/142649 describes biomarkers for im- proved diagnosis and prognosis of chronic kidney dis- ease.
WO 2017/040488 discloses methods of identify- ing subjects at risk of developing a chronic kidney disease.
SUMMARY
A method for determining whether a subject is at risk of developing a renal disease is disclosed. The method may comprise determining in a biological sample obtained from the subject a auantitative value of at least one biomarker of the following: - glycoprotein acetyls,
Q - a ratio of docosahexaenoic acid to total
N fatty acids, 3 30 - a ratio of linoleic acid to total fatty 2 acids, =E - a ratio of monounsaturated fatty acids and/or * of oleic acid to total fatty acids, > - a ratio of omega-3 fatty acids to total fatty = 35 acids,
S - a ratio of omega-6 fatty acids to total fatty acids,
- a ratio of saturated fatty acids to total fatty acids, - fatty acid degree of unsaturation, - docosahexaenoic acid, - linoleic acid, - monounsaturated fatty acids and/or oleic acid, - omega-3 fatty acids, - omega-6 fatty acids, - triglycerides in high-density lipoprotein (HDL), - triglycerides in low-density lipoprotein (LDL), - high-density lipoprotein (HDL) particle size, - low-density lipoprotein (LDL) particle size, - very-low-density lipoprotein (VLDL) particle size, - acetate, - citrate, - pyruvate, - alanine, - glutamine, - histidine, - isoleucine, - phenylalanine; and comparing the quantitative value(s) of the at
N least one biomarker to a control sample or to a control
N value; 3 30 wherein an increase or a decrease in the 2 quantitative value(s) of at least one biomarker, when
Ek compared to the control sample or to the control value, * is/are indicative of the subject having an increased > risk of developing the renal disease. = 35
S BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are included to provide a further understanding of the embodiments and constitute a part of this specification, illustrate various embodiments. In the drawings:
Figure la shows the relation of baseline concentrations of 25 blood biomarkers to the future development of Any Renal Disease (defined as a combined endpoint of any ICD-10 diagnoses within C64, I12 and/or
NOO-N29; here termed "Any Renal Disease”), when the biomarker concentrations are analysed in absolute concentrations and in quintiles of biomarker concentrations. Results are based on plasma samples from approximately 115 000 generally healthy individuals from the UK Biobank.
Figure 1b shows the cumulative risk for "Any
Renal Disease” during follow-up for the lowest, middle, and highest quintiles of the 25 blood biomarker concentrations.
Figure 2a shows the relation of the baseline concentrations of the 25 blood biomarkers to future development of 5 different categories of renal diseases (defined by ICD-10 subchapters), in the form of a heatmap. The results demonstrate that the 5 different renal disease subgroups all have highly similar associations with the 25 biomarkers measured by nuclear magnetic resonance (NMR) spectroscopy of plasma samples from generally healthy humans. & Figure 2b shows the consistency of the
N biomarker associations with the 5 different categories 3 30 of renal diseases, defined by ICD-10 subchapters, in 2 comparison to the direction of corresponding biomarker =E associations with "Any Renal Disease”. + Figure 3a shows the relation of baseline > biomarker levels to the future development of 13 = 35 specific renal diseases (defined by ICD-10 3-character
S diagnoses) in the form of a heatmap. The results demonstrate that the specific renal diseases defined by
3-character ICD-10 diagnosis codes all have highly similar associations with a broad panel of biomarkers measured by NMR spectroscopy of plasma samples from generally healthy humans. 5 Figure 3b shows the consistency of the biomarker associations with the specific renal diseases (defined by ICD-10 3-character diagnoses), in comparison to direction of the association with “Any Renal
Disease”.
Figure 4a shows the relation of baseline biomarker levels to the future development of 16 specific renal diseases (defined by ICD-10 4-character diagnoses) in the form of a heatmap. The results demonstrate that the specific renal diseases defined by 4-character ICD-10 diagnosis codes all have highly similar associations with a broad panel of biomarkers measured by NMR spectroscopy of plasma samples from generally healthy humans.
Figure 4b shows the consistency of the biomarker associations with the specific renal diseases (defined by ICD-10 4-character diagnoses), in comparison to direction of the association with "Any Renal
Disease”.
Figures 5a, 5b, and 5c show the relation of baseline biomarker levels to the future development of 5 different renal disease subgroups (defined by ICD-10 subchapters), in the form of forestplots of the hazard & ratios for incident disease onset.
N Figures 6a, 6b, 6c, 6d, 6e, 6f, and 6g show the 3 30 relation of baseline biomarker levels to the future 2 development of 13 specific renal diseases (defined by =E ICD-10 3-character diagnoses), in the form of + forestplots of the hazard ratios for incident disease > onset. = 35 Figures 7a, 7b, 7c, 7d, Je, 7£, 7g, and 7h show
S the relation of baseline biomarker levels to the future development of 16 specific renal diseases (defined by
ICD-10 4-character diagnoses), in the form of forestplots of the hazard ratios for incident disease onset.
Figure 8 shows the relation of baseline concentrations of the 25 blood biomarkers to the risk of death from Any Renal Disease as compared to the risk of future hospitalisation and/or death from Any Renal
Disease. The results demonstrate that the biomarkers are often stronger predictors for fatal events than for hospitalisation.
Figure 9 shows the relation of baseline concentrations of the 25 blood biomarkers to the risk of future development Any Renal Disease, when the associations are adjusted for standard variables (sex and assessment center), as compared to the associations adjusted also for estimated glomerular filtration rate (eGFR) commonly used for measuring kidney function. The results demonstrate that the biomarkers remain predictive after adjusting for eGFR.
Figure 10 shows an example of the relation of multi-biomarker scores to the risk of "Any Renal
Disease”. Selected examples of multi-biomarker scores are shown to illustrate the improved prediction attained by multi-biomarker scores as compared to individual biomarkers.
Figure lla shows an intended use case for a multi-biomarker score to predict the risk for developing & renal cancer among initially healthy humans.
N Figure 1l1lb shows that the prediction of the 3 30 risk for developing renal cancer works effectively for 2 people with different demographics and risk factor = profiles. * Figure 12a shows an intended use case for a > multi-biomarker score to predict the risk for developing = 35 glomerular diseases among initially healthy humans.
S Figure 12b shows that the prediction of the risk for developing glomerular diseases works effectively for people with different demographics and risk factor profiles.
Figure 13a shows an intended use case for a multi-biomarker score to predict the risk for developing renal tubulo-interstitial diseases among initially healthy humans.
Figure 13b shows that the prediction of the risk for developing renal tubulo-interstitial diseases works effectively for people with different demographics and risk factor profiles.
Figure 14a shows an intended use case for a multi-biomarker score to predict the risk for developing acute kidney failure and chronic kidney disease among initially healthy humans.
Figure 14b shows that the prediction of the risk for developing acute kidney failure and chronic kidney disease works effectively for people with different demographics and risk factor profiles.
Figure 15a shows an intended use case for a multi-biomarker score to predict the risk for developing urolithiasis among initially healthy humans.
Figure 15b shows that the prediction of the risk for urolithiasis works effectively for people with different demographics and risk factor profiles.
Figure 16a shows an intended use case for a multi-biomarker score to predict the risk for developing other disorders of kidney and ureter among initially
N healthy humans.
N Figure 16b shows that the prediction of the 3 30 risk for developing other disorders of kidney and ureter 2 works effectively for people with different demographics =E and risk factor profiles. a > DETAILED DESCRIPTION = 35 A method for determining whether a subject is
S at risk of developing a renal disease is disclosed.
The method may comprise determining in a biological sample obtained from the subject a quantitative value of at least one biomarker of the following: - glycoprotein acetyls, - a ratio of docosahexaenoic acid to total fatty acids, - a ratio of linoleic acid to total fatty acids, - a ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, - a ratio of omega-3 fatty acids to total fatty acids, - a ratio of omega-6 fatty acids to total fatty acids, - a ratio of saturated fatty acids to total fatty acids, - fatty acid degree of unsaturation, - docosahexaenoic acid, - linoleic acid, - monounsaturated fatty acids and/or oleic acid, - omega-3 fatty acids, - omega-6 fatty acids, - triglycerides in high-density lipoprotein (HDL), - triglycerides in low-density lipoprotein
N (LDL) ,
N - high-density lipoprotein (HDL) particle 3 30 size, 2 - low-density lipoprotein (LDL) particle size, =E - very-low-density lipoprotein (VLDL) particle + size, > - acetate, = 35 - citrate,
S - pyruvate, - alanine,
- glutamine, - histidine, - isoleucine, - phenylalanine; and comparing the quantitative value(s) of the at least one biomarker to a control sample or to a control value; wherein an increase or a decrease in the quantitative value(s) of the at least one biomarker, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing the renal disease.
Various blood biomarkers may be useful for predicting whether an individual person is at elevated risk of developing a broad range of renal diseases. Such biomarkers may be measured from biological samples, for example from blood samples or related biological fluids.
Biomarkers predictive of renal diseases could help to enable more effective screening and better tar- geted preventative treatment.
The method may comprise determining in a biological sample obtained from the subject a quantitative value of at least one biomarker of the following: - glycoprotein acetyls, - a ratio of docosahexaenoic acid to total fatty acids,
N - a ratio of linoleic acid to total fatty
N acids, 3 30 - a ratio of monounsaturated fatty acids and/or 2 of oleic acid to total fatty acids, =E - a ratio of omega-6 fatty acids to total fatty + acids, > - a ratio of saturated fatty acids to total = 35 fatty acids,
S - fatty acid degree of unsaturation, - docosahexaenoic acid,
- linoleic acid, - monounsaturated fatty acids and/or oleic acid, - omega-6 fatty acids, - triglycerides in high-density lipoprotein (HDL), - triglycerides in low-density lipoprotein (LDL), - high-density lipoprotein (HDL) particle size, - low-density lipoprotein (LDL) particle size, - very-low-density lipoprotein (VLDL) particle size, - acetate, - citrate, - pyruvate, - alanine, - glutamine, - histidine, - isoleucine, - phenylalanine; and comparing the quantitative value(s) of the at least one biomarker to a control sample or to a control value; wherein an increase or a decrease in the quantitative value(s) of the at least one biomarker, when compared to the control sample or to the control
N value, is/are indicative of the subject having an
N increased risk of developing the renal disease. 3 30 The method comprises determining a 2 guantitative value of glycoprotein acetyls. = In an embodiment, the method comprises + determining a quantitative value of the ratio of > docosahexaenoic acid to total fatty acids. = 35 In an embodiment, the method comprises
S determining a quantitative value of the ratio of linoleic acid to total fatty acids.
In an embodiment, the method comprises determining a quantitative value of the ratio of monounsaturated fatty acids and/or oleic acid to total fatty acids.
In an embodiment, the method comprises determining a quantitative value of the ratio of omega- 3 fatty acids to total fatty acids.
In an embodiment, the method comprises determining a quantitative value of the ratio of omega- 6 fatty acids to total fatty acids.
In an embodiment, the method comprises determining a quantitative value of fatty acid degree of unsaturation.
In an embodiment, the method comprises determining a quantitative value of docosahexaenoic acid.
In an embodiment, the method comprises determining a quantitative value of linoleic acid.
In an embodiment, the method comprises determining a quantitative value of monounsaturated fatty acids and/or oleic acid.
In an embodiment, the method comprises determining a quantitative value of omega-3 fatty acids.
In an embodiment, the method comprises determining a quantitative value of omega-6 fatty acids.
In an embodiment, the method comprises determining a quantitative value of triglycerides in
N high-density lipoprotein (HDL).
N In an embodiment, the method comprises 3 30 determining a quantitative value of triglycerides in 2 low-density lipoprotein (LDL). = In an embodiment, the method comprises + determining a quantitative value of high-density > lipoprotein (HDL) particle size. = 35 In an embodiment, the method comprises
S determining a quantitative value of low-density lipoprotein (LDL) particle size.
In an embodiment, the method comprises determining a quantitative value of very-low-density lipoprotein (VLDL) particle size.
In an embodiment, the method comprises determining a quantitative value of acetate.
In an embodiment, the method comprises determining a quantitative value of citrate.
In an embodiment, the method comprises determining a quantitative value of pyruvate.
In an embodiment, the method comprises determining a quantitative value of alanine.
In an embodiment, the method comprises determining a quantitative value of glutamine.
In an embodiment, the method comprises determining a quantitative value of histidine.
In an embodiment, the method comprises determining a quantitative value of isoleucine.
In an embodiment, the method comprises determining a quantitative value of phenylalanine.
The metabolic biomarker (s) described in this specification have been found to be significantly different, i.e. their quantitative values (such as amount and/or concentration) have been found to be significantly higher or lower, for subjects who later developed a renal disease. The biomarkers may be detected and quantified from blood, serum, or plasma, dry blood spots, or other suitable biological sample, & and may be used to determine the risk of developing the
N renal disease, either alone or in combination with other 3 30 biomarkers. 2 Furthermore, the biomarker (s) may
Ek significantly improve the possibility of identifying a subjects at risk for a renal disease, even in > combination with and/or when accounting for established = 35 risk factors that may currently be used for screening
S and risk prediction, such as age, sex, smoking status, use of alcohol, blood pressure, abnormalities in kidney structure, genetic risk and/or prior medical and/or family history of having renal diseases and/or other comorbidities, such as diabetes or cardiovascular diseases. The biomarkers described in this specification, alone or as a risk score (such as a multi- biomarker score), hazard ratio, odds ratio, and/or predicted absolute or relative risk, or in combination with other risk factors and tests, may improve prediction or even replace the need for other tests or measures. This may include improving prediction accuracy by complementing the predictive information from other risk factors, or by replacing the need for other analyses and tests, such as imaging tests to assess kidneys’ structure and size, blood tests for protein biomarkers such as C-rective protein (CRP) or cystatin
C or C-peptide, blood tests for creatinine and/or cystatin C to compute estimated glomerular filtration rate (eGFR), cystatin C estimated glomerular filtration rate (eGFR-cys) and/or creatinine-cystatin C estimated glomerular filtration rate (eGFRcr-cys) and/or test for the level of urine albumin and/or urine albumin to creatinine ratio (UACR). The biomarkers or the risk score, hazard ratio, odds ratio, and/or predicted absolute or relative risk according to one or more embodiments described in this specification may thus allow for efficiently assessing the risk for future development of a renal disease, also in conditions in & which other risk factor measures are not as feasible.
N The method may comprise determining in the 3 30 biological sample quantitative values of a plurality of 2 the biomarkers, such as two, three, four, five or more =E biomarkers. For example, the plurality of the biomarkers * may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, > 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 (i.e. = 35 at least 2, at least 3, at least 4, at least 5, at least
S 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25) or all of the biomarkers. The term "plurality of the biomarkers” may thus, within this specification, be understood as referring to any number (above one) of the biomarkers.
The term “plurality of the biomarkers” may thus be understood as referring to any number (above one) and/or combination or subset of the biomarkers described in this specification. Determining the plurality of the biomarkers may increase the accuracy of the prediction of whether the subject is at risk of developing the renal disease. In general, it may be that the higher the number of the biomarkers, the more accurate or predictive the method. However, even a single biomarker described in this specification may allow for or assist in determining whether the subject is at risk of developing the renal disease. The plurality of the biomarkers may be measured from the same biological sample or from separate biological samples and using the same analytical method or different analytical methods.
In an embodiment, the plurality of biomarkers may be a panel of a plurality of biomarkers.
In the context of this specification, the wording “comparing the quantitative value(s) of the biomarker(s) to a control sample or to a control value(s)” may be understood, as a skilled person would, & as referring to comparing the quantitative wvalue or
N values of the biomarker or biomarkers, to a control 3 30 sample or to a control value(s) either individually or 2 as a plurality of biomarkers (e.g. when a risk score is =E calculated from the quantitative values of a plurality * of biomarkers), depending e.g. on whether the > quantitative value of a single (individual) biomarker = 35 or the guantitative values of a plurality of biomarkers
S are determined.
In an embodiment, the method may comprise determining in the biological sample obtained from the subject a quantitative value of the following biomarkers: - glycoprotein acetyls, - the ratio of docosahexaenoic acid to total fatty acids, - the ratio of linoleic acid to total fatty acids, - the ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, - the ratio of omega-3 fatty acids to total fatty acids, - the ratio of omega-6 fatty acids to total fatty acids, - the ratio of saturated fatty acids to total fatty acids, - fatty acid degree of unsaturation, - docosahexaenoic acid, - linoleic acid, - monounsaturated fatty acids and/or oleic acid, - omega-3 fatty acids, - omega-6 fatty acids, - triglycerides in high-density lipoprotein (HDL), - triglycerides in low-density lipoprotein
N (LDL) ,
N - high-density lipoprotein (HDL) particle 3 30 size, 2 - low-density lipoprotein (LDL) particle size, =E - very-low-density lipoprotein (VLDL) particle + size, > - acetate, = 35 - citrate,
S - pyruvate, - alanine,
- glutamine, - histidine, - isoleucine, - phenylalanine; and comparing the quantitative wvalue(s) of the biomarkers to a control sample or to a control value(s); wherein an increase or a decrease in the guantitative value(s) of the biomarkers, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing the renal disease.
The at least one biomarker comprises or is glycoprotein acetyls. The method may further comprise determining a quantitative value of at least one of the other biomarkers described in this specification.
The subject may be human. The subject may be healthy or have an existing disease, such as an existing renal disease. Specifically, the subject may have an already existing form of a renal disease, and the risk for developing more severe forms of said disease and/or other renal diseases may be determined and/or calculated. The subject may, additionally or alternatively, be an animal, such as a mammal, for example, a non-human primate, a dog, a cat, a horse, or a rodent.
In the context of this specification, the term “biomarker” may refer to a biomarker, for example a
AN chemical or molecular marker, that may be found to be
N associated with a disease or a condition or the risk of 3 30 having or developing thereof. It does not necessarily 2 refer to a biomarker that would be statistically fully =E validated as having a specific effectiveness in a + clinical setting. The biomarker may be a metabolite, a > compound, a lipid, a protein, a moiety, a functional = 35 group, a composition, a combination of two or more
S metabolites and/or compounds, a (measurable or measured) guantity thereof, a ratio or other value derived thereof, or in principle any measurement reflecting a chemical and/or biological component that may be found associated with a disease or condition or the risk of having or developing thereof. The biomarkers and any combinations thereof, optionally in combination with further analyses and/or measures, may be used to measure a biological process indicative of the risk for developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis, and/or other disorder of kidney and/or ureter.
The disorder may refer to a category of renal diseases or to a specific disorder in this category. The disorder may be acute or occasional, or a chronic condition. The chronic condition may, in the context of this specification, be understood as persistent or otherwise long-lasting in its effects and/or a disease that comes with time.
The biomarker associations may be similar for the different renal diseases. Therefore, the same individual biomarkers and combinations of biomarkers may be extended to also predict the risk for specific renal diseases. Examples of such specific renal diseases include renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis, and/or & other disorder of kidney and/or ureter.
N Renal diseases described in this specification 3 30 may be classified as follows. "ICD-10” may be understood 2 as referring to the International Statistical Classifi-
Ek cation of Diseases and Related Health Problems 10th Re- + vision (ICD-10) - WHO Version for 2019. Similar diseases > classified or diagnosed by other disease classification = 35 systems than ICD-10, such as ICD-9 or ICD-11, may also
N apply.
The term “Any Renal Disease” may be understood as referring to any renal disease, disorder or condi- tion. Any Renal Disease may be understood as referring to any incident occurrence of ICD-10 diagnoses C64, I12 and/or N00-N29.
Renal disease Subgroups may be understood as referring to diseases and/or conditions classified within the ICD-10 subchapter diagnoses for renal dis- eases (N00-N08, N10-N16, N17-N19, N20-N23, N25-N29).
Specific renal diseases may be understood as referring to diseases and classified within the 3- character ICD-10 diagnoses for renal diseases (C64, I12,
N03, N08, N12, N13, N17, N18, N19, N20, N21, N23, N28) and/or within the 4-character ICD-10 diagnoses for renal diseases (I12.0, N03.9, N13.2, N13.3, N17.9, N18.2,
N18.3, N18.4, N18.5, N18.9, N20.0, N20.1, N21.0, N28.1,
N28.8, N28.9).
In an embodiment, the renal disease comprises or is a subgroup of renal diseases, such as defined by one or more 1ICD-10 subchapters described in this specification.
In an embodiment, the renal disease comprises or is a specific disease, such as a renal disease defined by ICD-10 3-character code diagnoses.
In an embodiment, the renal disease is a specific disease, such as a renal disease defined by
ICD-10 4-character code diagnoses.
N In an embodiment, the renal diseases comprises
N or is a disease among at least one of the following 3 30 renal disease subgroups: 2 - N00-N08: Glomerular diseases = - N10-N16: Renal tubulo interstitial diseases * - N17-N19: Acute kidney failure and chronic kidney > disease = 35 - N20-N23: Urolithiasis
S - N25-N29: Other disorders of kidney and ureter
In an embodiment, the renal disease comprises or is at least one of the following ICD-10 3-character diagnoses: - C64: Malignant neoplasm of kidney, except renal pelvis - I12: Hypertensive chronic kidney disease - N03: Chronic nephritic syndrome - N08: Glomerular disorders in diseases classified elsewhere = N12: Tubulo-interstitial nephritis, not specified as acute or chronic = N13: Obstructive and reflux uropathy - N17: Acute kidney failure - N18: Chronic kidney disease (CKD) - N19: Unspecified kidney failure - N20: Calculus of kidney and ureter = N21: Calculus of lower urinary tract - N23: Unspecified renal colic = N28: Other disorders of kidney and ureter, not elsewhere classified
In an embodiment, the renal disease is one of the following ICD-10 4-character diagnoses: - 1I12.0: Hypertensive CKD with stage 5 CKD or end stage renal disease - N03.9: Chronic nephritic syndrome with unspecified morphologic changes - N13.2: Hydronephrosis with renal and ureteral & calculous obstruction
N = N13.3: Other and unspecified hydronephrosis 3 30 = N17.9: Acute kidney failure, unspecified 2 - N18.2: Chronic kidney disease, stage 2 (mild) = - N18.3: Chronic kidney disease, stage 3 (moderate) * - N18.4: Chronic kidney disease, stage 4 (severe) > - N18.5: Chronic kidney disease, stage 5 = 35 = N18.9: Chronic kidney disease, unspecified
S - N20.0: Calculus of kidney = N20.1: Calculus of ureter
- N21.0: Calculus in bladder - N28.1: Cyst of kidney, acquired - N28.8: Other specified disorders of kidney and ureter - N28.9: Disorder of kidney and ureter, unspecified
In an embodiment, the renal disease may comprise or be death from a renal disease, such as a disease denoted by the ICD-10 codes listed above.
In an embodiment, the renal disease may comprise or be glomerular disease (N00-N08); renal tubulo interstitial disease (N10-N16); acute kidney failure and/or chronic kidney disease (N17-N19); and/or urolithiasis (N20-N23).
In an embodiment, the renal disease may com- prise or be malignant neoplasm of kidney, except renal pelvis (C64); hypertensive chronic kidney disease (112); chronic nephritic syndrome (N03); glomerular disorder in diseases classified elsewhere (N08); tubulo-inter- stitial nephritis, not specified as acute or chronic (N12); obstructive and/or reflux uropathy (N13); acute kidney failure (N17); chronic kidney disease (ckd) (N18); unspecified kidney failure (N19); calculus of kidney and ureter (N20); calculus of lower urinary tract (N21); and/or unspecified renal colic (N23).
In an embodiment, the renal disease may com- prise or be hypertensive CKD with stage 5 CKD or end stage renal disease (I12.0); chronic nephritic syndrome
N with unspecified morphologic changes (N03.9); hydro-
N nephrosis with renal and ureteral calculous obstruction 3 30 (N13.2); other and/or unspecified hydronephrosis 2 (N13.3); acute kidney failure, unspecified (N17.9); =E chronic kidney disease, stage 2 (mild) (N18.2); chronic * kidney disease, stage 3 (moderate) (N18.3); chronic kid- > ney disease, stage 4 (severe) (N18.4); chronic kidney = 35 disease, stage 5 (N18.5); chronic kidney disease, un-
S specified (N18.9); calculus of kidney (N20.0); calculus of ureter (N20.1); calculus in bladder (N21.0); cyst of kidney, acquired (N28.1).
In an embodiment, the renal disease may com- prise or be renal cancer, glomerular disease, renal tu- bulo-interstitial disease, acute kidney failure and/or chronic kidney disease, and/or urolithiasis.
In an embodiment, the renal disease 1s or comprises glomerular disease, renal tubulo interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis, a disorder of kidney and/or ureter, malignant neoplasm of kidney, such as malignant neoplasm of kidney except renal pelvis, hypertensive chronic kidney disease, chronic nephritic syndrome, glomerular disorder, such as a glomerular disorder in a disease classified elsewhere, tubulo-interstitial nephritis, such as acute or chronic tubulo-interstitial nephritis, obstructive and/or reflux uropathy, acute kidney failure, chronic kidney disease (CKD), unspecified kidney failure, calculus of kidney and/or ureter, calculus of lower urinary tract, and/or renal colic, such as unspecified renal colic.
The method may further comprise determining whether the subject is at risk of developing the renal disease using a risk score, hazard ratio, odds ratio, and/or predicted absolute or relative risk calculated on the basis of the quantitative value(s) of the at least one biomarker or of the plurality of the bi-
N omarkers.
N An increase or a decrease in the risk score, 3 30 hazard ratio, odds ratio, and/or predicted absolute risk 2 and/or relative risk may be indicative of the subject
Ek having an increased risk of developing the renal + disease. > The risk score, hazard ratio, odds ratio and/or = 35 predicted absolute risk or relative risk may be
S calculated based on any plurality, combination or subset of biomarkers described in this specification.
The risk score, hazard ratio, odds ratio, and/or predicted absolute risk or relative risk may be calculated e.g. as shown in the Examples below. For example, the plurality of biomarkers measured using a suitable method, for example with NMR spectroscopy, may be combined using regression algorithms and multivariate analyses and/or using machine learning analysis. Before regression analysis or machine learning, any missing values in the biomarkers may be imputed with the mean value of each biomarker for the dataset. A number of biomarkers, for example five, that may be considered most associated with the onset of the disease or condition may be selected for use in the prediction model. Other modelling approaches may be used to calculate a risk score and/or hazard ratio and/or predicted absolute risk or relative risk based on a combination or subset of individual biomarkers, i.e. a plurality of the biomarkers.
The risk score may be calculated e.g. as a weighted sum of individual biomarkers, i.e. a plurality of the biomarkers. The weighted sum may be e.g. in the form of a multi-biomarker score defined as Y; [B;*c;] +
Bo; where i is the index of summation over individual biomarkers, B; is the weighted coefficient attributed to biomarker i, ci; is the blood concentration of biomarker i, and Bois an intercept term.
For example, the risk score can be defined as:
N Bi*concentration(glycoprotein acetyls) + Box
N concentration (monounsaturated fatty acid ratio to total 3 30 fatty acids) + 837 concentration (fatty acid degree of 2 unsaturation) + Bo, where 61 Bo, B3 are multipliers =E for each biomarker according to the association + magnitude with risk of the renal disease and fo is an > intercept term. As a skilled person will understand, the = 35 biomarkers mentioned in this example may be replaced by
S any other biomarker(s) described in this specification.
In general, the more biomarkers are included in the risk score, the stronger the predictive performance may become. When additional biomarkers are included in the risk score, the Bi weights may change for all biomarkers according to the optimal combination for the prediction of the renal disease.
The risk score, hazard ratio, odds ratio, and/or predicted relative risk and/or absolute risk may be calculated on the basis of at least one further measure, for example a characteristic of the subject.
Such characteristics may be determined prior to, simultaneously, or after the biological sample is obtained from the subject. As a skilled person will understand, some of the characteristics may be information collected e.g. using a questionnaire or clinical data collected earlier. Some of the characteristics may be determined (or may have been determined) by biochemical or clinical diagnostic measurements and/or medical diagnosis. Such characteristics could include, for example, one or more of age, height, weight, body mass index, race or ethnic group, smoking, and/or family history of renal diseases.
In an embodiment, the characteristics may comprise age, sex, smoking status, use of alcohol, blood pressure, abnormalities in kidney structure, genetic risk and/or prior medical and/or family history of having renal diseases and/or other comorbidities, such as diabetes or cardiovascular diseases. In an embodiment, the
N characteristics may comprise other analyses and tests,
N such as an imaging test to assess kidneys’ structure and 3 30 size, a blood test for protein biomarkers such as C- 2 rective protein (CRP) or cystatin C or C-peptide, blood =E test for creatinine and/or cystatin C to compute > estimated glomerular filtration rate (eGFR), cystatin C > estimated glomerular filtration rate (eGFR-cys) and/or = 35 creatinine-cystatin C estimated glomerular filtration
S rate (eGFRcr-cys) and/or test for the level of urine albumin and/or urine albumin to creatinine ratio (UACR).
The risk prediction for the renal disease guided based on one or more of the biomarkers can be used to guide preventative efforts, such as healthy diet, alcohol and smoking awareness, physical activity, lower salt intake, increased hydration and/or clinical screening frequency and/or pharmacological treatment decisions. For example, the information of the future risk for the renal disease can be used for guiding treatments to balance the amount of fluids in the blood, such as diuretics, medications and/or dietary choices to control the amount of potassium in blood and/or medications and/or supplements to restore blood calcium levels.
In the context of this specification, the term “glycoprotein acetyls”, “glycoprotein acetylation”, or “GlycA” may refer to the abundance of circulating glycated proteins, and/or to a nuclear magnetic resonance spectroscopy (NMR) signal that represents the abundance of circulating glycated proteins, i.e. N- acetylated glycoproteins. Glycoprotein acetyls may include signals from a plurality of different glycoproteins, including e.g. alpha-1-acid glycoprotein, alpha-1 antitrypsin, haptoglobin, transferrin, and/or alpha-1 antichymotrypsin. In the scientific literature on cardiometabolic biomarkers, the terms "glycoprotein acetyls” or "GlycA” may commonly refer to the NMR signal of circulating glycated proteins & (e.g. Ritchie et al, Cell Systems 2015 1(4):293-301;
N Connelly et al, J Transl Med. 2017;15(1):219). 3 30 Glycoprotein acetyls and a method for measuring them is 2 described e.g. in Kettunen et al., 2018, Circ Genom
Ek Precis Med. 11:e002234 and Soininen et al., 2009, + Analyst 134, 1781-1785. There may be benefits of using > the NMR signal of glycoprotein acetyls for risk = 35 prediction above measurement of the individual proteins
S contributing to the NMR signal, for instance better analytical accuracy and stability over time, as well as lower costs of the measurement and the possibility to measure the NMR signal simultaneously with many other biomarkers.
In the context of this specification, the term "omega-3 fatty acids” may refer to total omega-3 fatty acids, i.e. the total omega-3 fatty acid amounts and/or concentrations, i.e. the sum of different omega-3 fatty acids. Omega-3 fatty acids are polyunsaturated fatty acids. In omega-3 fatty acids, the last double bond in the fatty acid chain is the third bond counting from the methyl end. Docosahexaenoic acid is an example of an omega-3 fatty acid.
In the context of this specification, the term "omega-6 fatty acids” may refer to total omega-6 fatty acids, i.e. the total omega-6 fatty acid amounts and/or concentrations, i.e. the sum of the amounts and/or concentrations of different omega-6 fatty acids. Omega- 6 fatty acids are polyunsaturated fatty acids. In omega- 6 fatty acids, the last double bond in the fatty acid chain is the sixth bond counting from the methyl end.
In one embodiment, the omega-6 fatty acid may comprise or be linoleic acid. Linoleic acid (18:20-6) is the most abundant type of omega-6 fatty acids, and may therefore be considered as a good approximation for total omega-6 fatty acids for risk prediction of the renal disease.
In the context of this specification, the term & “monounsaturated fatty acids” (MUFAs) may refer to total
N monounsaturated fatty acids, i.e. the total MUFA amounts 3 30 and/or concentrations. Monounsaturated fatty acids may, 2 alternatively, refer to oleic acid, which is the most
Ek abundant monounsaturated fatty acid in human serum. * Monounsaturated fatty acids have one double bond in > their fatty acid chain. The monounsaturated fatty acids = 35 may include omega-9 and omega-7 fatty acids. Oleic acid
S (18:10-9), palmitoleic acid (16:10-7) and cis-vaccenic acid (18:10-7) are examples of common monounsaturated fatty acids in human serun.
In one embodiment, the monounsaturated fatty acid may comprise or be oleic acid. Oleic acid is the most abundant monounsaturated fatty acid, and may therefore be considered as a good approximation for total monounsaturated fatty acids for risk prediction of the renal disease.
For all fatty acid measures, including omega- 6, docosahexaenoic acid, linoleic acid and/or monounsaturated fatty acids, the fatty acid measures may include blood (or serum/plasma) free fatty acids, bound fatty acids and esterified fatty acids. Esterified fatty acids may, for example, be esterified to glycerol as in triglycerides, diglycerides, monoglycerides, or phosphoglycerides, or to cholesterol as in cholesterol esters.
In the context of this specification, the term “fatty acid degree of unsaturation” or “unsaturation” may be understood as referring to the number of double bonds in total fatty acids, for example the average number of double bonds in total fatty acids.
In the context of this specification, the term “HDL” refers to high-density lipoprotein.
In the context of this specification, the term “LDL” refers to low-density lipoprotein.
In the context of this specification, the term
N “WLDL” refers to very-low-density lipoprotein.
N In the context of this specification, the 3 30 phrase “low-density lipoprotein (LDL) triglycerides”, 2 “high-density lipoprotein (HDL) triglycerides”,
Ek “triglycerides in HDL (high-density lipoprotein)”, or * “triglycerides in LDL (low-density lipoprotein)”, may > be understood as referring to total triglyceride = 35 concentration in said lipoprotein class or subfraction.
S In the context of this specification, the phrase "high-density lipoprotein (HDL) particle size”,
“low-density lipoprotein (LDL) particle size”, or "very- low-density lipoprotein (VLDL) particle size”, may be understood as referring to the average diameter for the particles in said lipoprotein class or subfraction.
In the context of this specification, the term “acetate” may refer to the acetate molecule and/or acetic acid, for example in blood, plasma or serum or related biofluids.
In the context of this specification, the term “citrate” may refer to the citrate molecule and/or citric acid, for example in blood, plasma or serum or related biofluids.
In the context of this specification, the term “pyruvate” may refer to the citrate molecule and/or pyruvic acid, for example in blood, plasma or serum or related biofluids.
In the context of this specification, the term “alanine” may refer to the alanine amino acid, for example in blood, plasma or serum or related biofluids.
In the context of this specification, the term “glutamine” may refer to the glutamine amino acid, for example in blood, plasma or serum or related biofluids.
In the context of this specification, the term “histidine” may refer to the histidine amino acid, for example in blood, plasma or serum or related biofluids.
In the context of this specification, the term “isoleucine” may refer to the isoleucine amino acid, for
N example in blood, plasma or serum or related biofluids.
N In the context of this specification, the term 3 30 “phenylalanine” may refer to the phenylalanine amino 2 acid, for example in blood, plasma or serum or related =E biofluids. > In the context of this specification, the term > "guantitative value” may refer to any quantitative value = 35 characterizing the amount and/or concentration of a
S biomarker. For example, it may be the amount or concentration of the biomarker in the biological sample,
or it may be a signal derived from nuclear magnetic resonance spectroscopy (NMR) or other method suitable for detecting the biomarker in a auantitative manner.
Such a signal may be indicative of or may correlate with the amount or concentration of the biomarker. It may also be a quantitative value calculated from one or more signals derived from NMR measurements or from other measurements. Ouantitative values may, additionally or alternatively, be measured using a variety of technigues. Such methods may include mass spectrometry (MS), gas chromatography combined with MS, high performance liguid chromatography alone or combined with
MS, immunoturbidimetric measurements, ultracentrifugation, ion mobility, enzymatic analyses, colorimetric or fluorometric analyses, immunoblot analysis, immunohistochemical methods (e.g. in situ methods based on antibody detection of metabolites), and immunoassays (e.g. ELISA). Examples of various methods are set out below. The method used to determine the quantitative value(s) in the subject may be the same method that is used to determine the quantitative value(s) in a control subject/control subjects or in a control sample/control samples.
The quantitative value, or the initial quantitative value, of the at least one biomarker, or the plurality of the biomarkers, is measured using nuclear magnetic resonance (NMR) spectroscopy, for
N example H-NMR. The at least one additional biomarker,
N or the plurality of the additional biomarkers, may also 3 30 be measured using NMR. NMR may provide a particularly 2 efficient and fast way to measure biomarkers, including
Ek a large number of biomarkers simultaneously, and can + provide quantitative values for them. NMR also typically > requires very little sample pre-treatment or = 35 preparation. The biomarkers measured with NMR can
S effectively be measured for large amounts of samples using an assay for blood (serum or plasma) NMR metabolomics previously published by Soininen et al., 2015, Circulation: Cardiovascular Genetics 8, 212-206 (DOT: 10.1161/CIRCGENETICS.114.000216) ; Soininen et al., 2009, Analyst 134, 1781-1785; and Wirtz et al., 2017, American Journal of Epidemiology 186 (9), 1084- 1096 (DOI: 10.1093/aje/kwx016). This provides data on 250 biomarkers per sample as described in detail in the above scientific papers.
The (initial) quantitative value of the at least one biomarker is/are measured using nuclear magnetic resonance spectroscopy.
However, quantitative values for various biomarkers described in this specification may also be performed by technigues other than NMR. For example, mass spectrometry (MS), enzymatic methods, antibody- based detection methods, or other biochemical or chemical methods may be contemplated, depending on the biomarker.
For example, glycoprotein acetyls can be measured or approximated by immunoturbidimetric measurements of alpha-1-acid glycoprotein, haptoglobin, alpha-l-antitrypsin, and transferrin (e.g. as described in Ritchie et al., 2015, Cell Syst. 28;1(4):293-301).
F.g. monounsaturated fatty acids, saturated fatty acids, and omega-6 fatty acids can be quantified (i.e. their quantitative values may be determined) by serum total fatty acid composition using gas
N chromatography (for example, as described in Jula et
N al., 2005, Arterioscler Thromb Vasc Biol 25, 2152-2159). 3 30 In the context of this specification, the term 2 “sample” or "biological sample” may refer to any =E biological sample obtained from a subject or a group or + population of subjects. The sample may be fresh, frozen, > or dry. = 35 The biological sample may comprise or be, for
S example, a blood sample, a plasma sample, a serum sample, or a fraction or a sample derived therefrom. The biological sample may be, for example, a fasting blood sample, a fasting plasma sample, a fasting serum sample, or a fraction obtainable therefrom. However, the biological sample does not necessarily have to be a fasting sample. The blood sample may be a venous blood sample.
The blood sample may be a dry blood sample. The dry blood sample may be a dried whole blood sample, a dried plasma sample, a dried serum sample, or a dried sample derived therefrom.
The biological sample may be obtained from the subject prior to determining the quantitative value of the at least one biomarker. Taking a blood sample or a tissue sample of a subject or patient is a part of normal clinical practice. The collected blood or tissue sample can be prepared and serum or plasma can be separated using techniques well known to a skilled person. Methods for separating one or more fractions from biological samples, such as blood samples or tissue samples, are also available to a skilled person. The term “fraction” may, in the context of this specification, also refer to a portion or a component of the biological sample separated according to one or more physical properties, for instance solubility, hydrophilicity or hydrophobicity, density, or molecular size.
In the context of this specification, the term “control sample” may refer to a sample obtained from a
N subject and known not to suffer from the disease or
N condition or not being at risk of having or developing 3 30 the disease or condition. The control sample may be 2 matched. In an embodiment, the control sample may be a =E biological sample from a healthy individual or a * generalized population of healthy individuals. The term > "control value” may be understood as a value obtainable = 35 from the control sample and/or a quantitative value
S derivable therefrom. For example, it may be possible to calculate a threshold value from control samples and/or control values, above or below which the risk of developing the disease or condition is elevated. In other words, a value higher or lower (depending on the biomarker, risk score, hazard ratio, and/or predicted absolute risk or relative risk) than the threshold value may be indicative of the subject having an increased risk of developing the disease or condition.
An increase or a decrease in the quantitative value(s) of the at least one biomarker, or the plurality of the biomarkers, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of having or developing the disease or condition. Whether an increase or a decrease is indicative of the subject having an increased risk of developing the disease or condition, may depend on the biomarker.
A 1.2-fold, 1.5-fold, or for example 2-fold, or 3-fold, increase or a decrease in the quantitative value(s) of the at least one biomarker (or in an individual biomarker of the plurality of the biomarkers) when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the disease or condition.
In an embodiment, an increase in the quantitative value of glycoprotein acetyls, when compared to the control sample or to the control value, may be indicative of the subject having an increased
N risk of developing the renal disease, such as renal
N cancer, a glomerular disease, a renal tubulo- 3 30 interstitial disease, acute kidney failure and/or 2 chronic kidney disease, urolithiasis and/or other
Ek disorder of kidney and/or ureter. > In an embodiment, a decrease in the > quantitative value of the ratio of docosahexaenoic acid = 35 to total fatty acids, when compared to the control
S sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of the ratio of linoleic acid to total fatty acids, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, an increase in the quantitative value of the ratio of monounsaturated fatty acids and/or oleic acid to total fatty acids, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo- interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of the ratio of omega-3 fatty acids to total fatty acids, when compared to the control sample or to the control value, may be indicative of the
N subject having an increased risk of developing the renal
N disease, such as renal cancer, a glomerular disease, a 3 30 renal tubulo-interstitial disease, acute kidney failure 2 and/or chronic kidney disease, urolithiasis and/or other
Ek disorder of kidney and/or ureter. > In an embodiment, a decrease in the > quantitative value of the ratio of omega-6 fatty acids = 35 to total fatty acids, when compared to the control
S sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of fatty acid degree of unsaturation, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo- interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of docosahexaenoic acid, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo- interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of linoleic acid, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a
N glomerular disease, a renal tubulo-interstitial
N disease, acute kidney failure and/or chronic kidney 3 30 disease, urolithiasis and/or other disorder of kidney 2 and/or ureter. =E In an embodiment, an increase in the + guantitative value of monounsaturated fatty acids and/or > oleic acid, when compared to the control sample or to = 35 the control value, may be indicative of the subject
S having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of omega-3 fatty acids, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the guantitative value of omega-6 fatty acids, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, an increase in the guantitative value of triglycerides in high-density lipoprotein (HDL), when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a & renal tubulo-interstitial disease, acute kidney failure
N and/or chronic kidney disease, urolithiasis and/or other 3 30 disorder of kidney and/or ureter. 2 In an embodiment, an increase in the =E quantitative value of triglycerides in low-density + lipoprotein (LDL), when compared to the control sample > or to the control value, may be indicative of the subject = 35 having an increased risk of developing the renal
S disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of high-density lipoprotein (HDL) particle size, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of low-density lipoprotein (LDL) particle size, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, an increase in the quantitative value of very-low-density lipoprotein (VLDL) particle size, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure & and/or chronic kidney disease, urolithiasis and/or other
N disorder of kidney and/or ureter. 3 30 In an embodiment, a decrease in the 2 quantitative value of acetate, when compared to the
Ek control sample or to the control value, may be * indicative of the subject having an increased risk of > developing the renal disease, such as renal cancer, a = 35 glomerular disease, a renal tubulo-interstitial
S disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, an increase in the quantitative value of citrate, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, an increase in the quantitative value of pyruvate, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, an increase in the quantitative value of alanine, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney
N disease, urolithiasis and/or other disorder of kidney
N and/or ureter. 3 30 In an embodiment, a decrease in the 2 quantitative value of glutamine, when compared to the
Ek control sample or to the control value, may be * indicative of the subject having an increased risk of > developing the renal disease, such as renal cancer, a = 35 glomerular disease, a renal tubulo-interstitial
S disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, a decrease in the quantitative value of histidine, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, an increase in the quantitative value of isoleucine when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter.
In an embodiment, an increase in the quantitative value of phenylalanine, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo-interstitial disease, acute kidney failure and/or chronic kidney
N disease, urolithiasis and/or other disorder of kidney
N and/or ureter. 3 30 In an embodiment, a risk score defined as %B, 2 + Bat concentration (glycoprotein acetyls) + Bor =E concentration (fatty acid measure), where fy is an + intercept term, Bi is the weighted coefficient > attributed to the concentration of glycoprotein acetyls, = 35 Bo is the weighted coefficient attributed to the fatty
S acid measure, may be indicative of the subject having an increased risk of developing the renal disease, such as renal cancer, a glomerular disease, a renal tubulo- interstitial disease, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other disorder of kidney and/or ureter. The fatty acid measure may be one or more of the following fatty acids or their ratio to total fatty acids: docosahexaenoic acid, linoleic acid, omega-3 fatty acids, omega-6 fatty acids, monounsaturated fatty acids and/or fatty acid degree of unsaturation.
The term "combination” may, at least in some embodiments, be understood such that the method comprises using a risk score, hazard ratio, odds ratio, and/or predicted absolute risk or relative risk calculated on the basis of the auantitative value(s) of the biomarkers. For example, if quantitative values of both glycoprotein acetyls and monounsaturated fatty acids are determined, the quantitative values of both biomarkers may be compared to the control sample or the control value separately, or a risk score, hazard ratio, odds ratio, and/or predicted absolute risk or relative risk calculated on the basis of the quantitative value(s) of both the biomarkers, and the risk score, hazard ratio, odds ratio, and/or predicted absolute risk or relative risk may be compared to the control sample or the control value.
In an embodiment, the method may comprise determining in the biological sample obtained from the & subject a quantitative value of the following
N biomarkers: 3 30 - glycoprotein acetyls, 2 - at least one fatty acid measure(s) of the =E following fatty acids or their ratio to total fatty * acids: docosahexaenoic acid, linoleic acid, omega-3 > fatty acids, omega-6 fatty acids, monounsaturated fatty = 35 acids, saturated fatty acids and/or fatty acid degree
S of unsaturation; and comparing the quantitative wvalue(s) of the biomarkers and/or a combination thereof to a control sample or to a control value(s); wherein an increase or a decrease in the quantitative value(s) of the biomarkers and/or the combination thereof, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing the renal disease. An increase in the quantitative value of glycoprotein acetyls and a decrease in the quantitative value of docosahexaenoic and/or linoleic acid and/or omega-3 fatty acids and/or omega-6 fatty acids and/or fatty acid degree of unsaturation and/or their ratio to total fatty acids, and/or an increase in the guantitative value of monounsaturated fatty acids and/or its ratio to total fatty acids, when compared to the control sample or to the control value, may be indicative of the subject having an increased risk of developing the renal disease.
EXAMPLES
Reference will now be made in detail to various embodiments, an example of which is illustrated in the accompanying drawings. The description below discloses some embodiments in such a detail that a person skilled in the art is able to utilize the embodiments based on the disclosure. Not all steps or features of the & embodiments are discussed in detail, as many of the
N steps or features will be obvious for the person skilled 3 30 in the art based on this specification. 3
I Abbreviations used in the Figures: + DHA %: Ratio of docosahexaenoic acid to total > fatty acids = 35 LA%: Ratio of linoleic acid to total fatty
S acids
MUFA %: Ratio of monounsaturated fatty acids to total fatty acids
Omega-3 %: Ratio of omega-3 fatty acids to total fatty acids
Omega-6 %: Ratio of omega-6 fatty acids to total fatty acids
Unsaturation: Fatty acid degree of unsaturation
DHA: Docosahexaenoic acid
LA: Linoleic acid
MUFA: Monounsaturated fatty acids
Omega-6: Omega-6 fatty acids
Omega-3: Omega-3 fatty acids
SFA: Saturated fatty acids
HDL: High-density lipoprotein
IDL: Low-density lipoprotein
VLDL: Very-low-density lipoprotein
HDL-TG: Triglycerides in high-density lipoprotein (HDL)
LDL-TG: Triglycerides in low-density lipoprotein (HDL)
CI: confidence interval
SD: standard deviation
BMI: Body mass index
EXAMPLE 1
Biomarker measures quantified by nuclear
N magnetic resonance (NMR) were investigated as to whether
N they could be predictive of a renal disease, such as 3 30 renal cancer, glomerular diseases, renal tubulo- 2 interstitial diseases, acute kidney failure and/or =E chronic kidney disease, urolithiasis and/or other + disorders of kidney and ureter. All analyses were > conducted based on the UK Biobank, with approximately = 35 115 000 study participants with blood biomarker data
S from NMR spectroscopy available.
Study population
Details of the design of the UK Biobank have been reported by Sudlow et al 2015, PLoS Med. 2015;12(3) :e1001779. Briefly, UK Biobank recruited 502 639 participants aged 37-73 years in 22 assessment centres across the UK. All participants provided written informed consent and ethical approval was obtained from the North West Multi-Center Research Ethics Committee.
Blood samples were drawn at baseline between 2007 and 2010. No selection criteria were applied to the sampling.
Biomarker profiling
From the entire UK Biobank population, a random subset of baseline plasma samples from 118 466 individuals were measured using the Nightingale NMR biomarker platform (Nightingale Health Ltd, Finland).
This blood analysis method provides simultaneous quantification of many blood biomarkers, including lipoprotein lipids, circulating fatty acids, and various low-molecular weight metabolites including amino acids, ketone bodies and gluconeogenesis-related metabolites in molar concentration units. Technical details and epidemiological applications have been reviewed (Soininen et al 2015, Circ Cardiovasc Genet; 2015;8:192- 206; Wirtz et al 2017, Am J Epidemiol 2017;186:1084- 1096). Values outside four interquartile ranges from median were considered as outliers and excluded.
N Epidemiological analyses of biomarker
N relations with the risk of a renal disease 3 30 The blood biomarker associations with the risk 2 for a renal disease were conducted based on UK Biobank
Ek data. Analyses focused on the relation of the biomarkers + to the occurrence of a renal disease after the blood > samples were collected, to determine if the individual = 35 biomarkers associate with the risk for future develop-
S ment of a renal disease. Examples using multi-biomarker scores, in the form weighted sums of biomarkers, were also explored to see if they could be predictive even more strongly than each individual biomarker.
Information on the disease events occurring af- ter the blood samplings for all study participants were recorded from UK Hospital Episode Statistics data and death registries. All analyses are based on first oc- currence of diagnosis, so that individuals with recorded diagnosis of the given disease prior to blood sampling were omitted from the statistical analyses. A composite endpoint of Any Renal Disease was defined based on any incident occurrence of ICD-10 diagnoses C64, I12 and/or
NOO-N29. More refined subtypes of the renal diseases were defined according to the ICD-10 diagnoses listed in Table 1.
The registry-based follow-up was from blood sampling in 2007-2010 through to 2020 (approximately 1 100 000 person-years). Specific diseases which had <150 disease events recorded during follow-up were left out of scope.
For biomarker association testing, Cox propor- tional-hazard regression models adjusted for age, sex, and UK Biobank assessment centre were used. Results were plotted in magnitudes per standard deviation of each biomarker measure to allow direct comparison of associ- ation magnitudes.
Summary of results & Baseline characteristics of the study population for
N biomarker analyses vs future risk of a renal disease are 3 30 shown in Table 1. The number of incident disease events 2 occurring after the blood sampling is listed for all the =E conditions analysed. a > Table 1: Clinical characteristics of study = 35 participants and the number of incident disease events
S analysed.
samples analysed 118 456
Population sample of study volun- teers from
Study setting the UK mpu blood sampling 10-14 vears
Number of in- dividuals who developed the specified disease after
Diseases with similar biomarker rela- the blood tions sampling
La
I0D-10 subchaptersy 0 n eases cy chronic kidney disease >
O N25-N29: Other disorders of kidney and 1210 0 character 10010 codes) —— —,—,","">>55)b
N cept renal pelvis ease classified elsewhere specified as acute or chronic
CKD or end stage renal disease unspecified morphologic changes ureteral calculous obstruction nephrosis :
S fied 2 (mild) - N18.3: Chronic kidney disease, stage 3 2184 m
LO (severe) 3 fied kidney and ureter unspecified
Figure la shows the hazard ratios for the 25 blood biomarkers with the future risk of Any Renal Dis- ease (ICD-10 codes C64, I12 and/or N00-N29). The left- hand side of the figure shows the hazard ratios when the biomarkers are analysed in absolute concentrations, scaled to standard deviations of the study population.
The right-hand side shows the corresponding hazard ra- tios when individuals in the highest quintile of the biomarker concentration are compared to those in the lowest guintile. The results are based on statistical analyses of over 115 000 individuals from the UK Bi- obank, out of whom 8 671 developed a renal disease (de- fined as diagnoses C64, I12 and/or N00-N29 in the hos- pital registries, or in the death records) during ap- proximately 10 years of follow-up. The analyses were o adjusted for age, sex, and UK Biobank assessment centre
N in Cox proportional-hazard regression models. P-values
N were P<0.0001 (corresponding to multiple testing cor- <Q 20 rection) for all associations. These results demonstrate 3 that the 25 individual biomarkers are predictive of the
E risk for a renal disease in general population settings. o Figure 1b shows the Kaplan-Meier plots of the = cumulative risk for a renal disease for each of the 25
N 25 blood biomarkers according to the lowest, middle, and i highest guintiles of biomarker concentrations. The re- sults are based on statistical analyses of over 115 000 individuals from the UK Biobank, out of whom 8 671 de- veloped a renal disease. These results further demon- strate that the 25 individual biomarkers are predictive of the risk for a renal disease in general population settings.
Figure 2a shows the hazard ratios for the 25 blood biomarkers for the future onset of 65 subgroups of renal diseases, defined by ICD-10 subchapters. The results illustrate that the pattern of biomarker asso- ciations is highly consistent for the 5 different sub- types of renal diseases.
Figure 2b shows the consistency of the bi- omarker associations with the 5 renal disease subgroups (defined by ICD-10 subchapters) compared to the "Any
Renal Disease” definition. The biomarker associations were all in the same direction of association as for “Any Renal Disease” or not statistically significant in the discordant direction. Any biomarker combination that strongly predicts "Any Renal Disease” will therefore also be predictive of all the listed renal disease sub- groups.
Figure 3a shows the hazard ratios for the 25 blood biomarkers for future onset of 13 specific renal diseases, defined by 3-character ICD-10 diagnosis codes.
The results illustrate that the pattern of biomarker associations is highly consistent for all the 13 spe- cific disorders.
N Figure 3b shows the consistency of the bi-
N omarker associations with the 13 specific renal diseases 3 30 (defined by 3-character ICD-10 diagnosis codes) compared 2 to the "Any Renal Disease” definition. Generally, the =E biomarker associations are all in the same direction of * association as for “Any Renal Disease” or not statisti- > cally significant in the discordant direction. Any bi- = 35 omarker combination that strongly predicts "Any Renal
S Disease” will therefore also be predictive of all the listed specific renal diseases.
Figure 4a shows the hazard ratios for the 25 blood biomarkers for future onset of 16 specific renal diseases, defined by 4-character ICD-10 diagnosis codes.
The results illustrate that the pattern of biomarker associations is highly consistent for all the 16 spe- cific disorders.
Figure 4b shows the consistency of the bi- omarker associations with the 16 specific renal diseases (defined by 4-character ICD-10 diagnosis codes) compared to the "Any Renal Disease” definition. Generally, the biomarker associations are all in the same direction of association as for "Any Renal Disease” or not statisti- cally significant in the discordant direction. Any bi- omarker combination that strongly predicts "Any Renal
Disease” will therefore also be predictive of all the listed specific renal diseases.
Figures 5a-c show the hazard ratios for the 25 blood biomarkers with future onset of each of the 5 renal disease subgroups (defined by ICD-10 subchapters) studied here. The hazard ratios are shown in absolute concentrations, scaled to the standard deviation of each biomarker. The results are based on statistical analyses of over 115 000 individuals from the UK Biobank; the number of individuals who developed the disorder during approximately 10 years of follow-up is indicated on the top of each plot. Filled circles denote that the P-value for association was P<0.0001 (corresponding to multiple
N testing correction), and open circles denote that the
N P-value for association was P20.0001. The analyses were 3 30 adjusted for age, sex, and UK Biobank assessment centre 2 using Cox proportional-hazard regression models.
Ek Figures 6a-g show the hazard ratios for the 25 + blood biomarkers with future onset of each of the 14 > specific renal diseases (defined by ICD-10 3-character = 35 diagnosis codes) studied here. The hazard ratios are
S shown in absolute concentrations, scaled to the standard deviation of each biomarker. The results are based on statistical analyses of over 115 000 individuals from the UK Biobank; the number of individuals who developed the specific disease during approximately 10 vears of follow-up is indicated on the top of each plot. Filled circles denote that the P-value for association was
P<0.0001 (corresponding to multiple testing correc- tion), and open circles denote that the P-value for association was P20.0001. The analyses were adjusted for age, sex, and UK Biobank assessment centre using Cox proportional-hazard regression models.
Figures 7a-h show the hazard ratios for the 25 blood biomarkers with future onset of each of the 16 specific renal diseases (defined by ICD-10 4-character diagnosis codes) studied here. The hazard ratios are shown in absolute concentrations, scaled to the standard deviation of each biomarker. The results are based on statistical analyses of over 115 000 individuals from the UK Biobank; the number of individuals who developed the specific disease during approximately 10 vears of follow-up is indicated on the top of each plot. Filled circles denote that the P-value for association was
P<0.0001 (corresponding to multiple testing correc- tion), and open circles denote that the P-value for association was P20.0001. The analyses were adjusted for age, sex, and UK Biobank assessment centre using Cox proportional-hazard regression models.
Figure 8 shows hazard ratios for the 25 blood
N biomarkers with future death from ‘Any Renal Disease’
N (in black). For comparison are also shown the hazard 3 30 ratios for incidence of Any Renal Disease (i.e. combined 2 non-fatal and fatal events, in gray). The hazard ratios
Ek are shown for biomarkers analysed in absolute concen- * trations, scaled to standard deviations of the study > population. These results demonstrate that the bi- = 35 omarkers are often stronger predictors of fatal renal
S disease events. The results were similar for the biomarker associations with fatal events due to specific renal disease listed in Table 1 as those shown here.
Figure 9 shows hazard ratios for the 25 blood biomarkers with the risk of future development Any Renal
Disease, when the associations are adjusted for standard variables (sex and assessment center), as compared to the associations adjusted also for estimated glomerular filtration rate (eGFR) commonly used for measuring kid- ney function. The results demonstrate that the bi- omarkers remain predictive after adjusting for eGFR.
Figure 10 shows examples of stronger associa- tion results with Any Renal Disease when two or more biomarkers are combined. The hazard ratios with the fu- ture risk of Any Renal Disease (composite endpoint of
ICD-10 codes C64, Il2 and/or N00-N29) are shown for selected combinations of pairs of biomarkers, and exam- ples of multi-biomarker scores. The results were similar with many other combinations, in particular inclusion of different fatty acid measures in addition to glyco- protein acetyls. The multi-biomarker scores are combined in the form of Y; (B:*c;l + Bo; where i is the index of summation over individual biomarkers, B;is the weighted coefficient attributed to biomarker i, c; is the blood concentration of biomarker i and B, is an intercept term. 8. multipliers are defined according to the mul- tivariate association magnitude with the risk for Any
Renal Disease, examined in the statistical analyses of & the UK Biobank study for the respective combination of
N biomarkers. The enhancements in association magnitudes 3 30 were similar for the specific types of renal diseases 2 listed in Table 1 as those shown here for Any Renal = Disease. a > Illustrations of intended use: multi-biomarker scores = 35 for risk prediction of a renal disease
S For illustration of intended applications re- lated to the prediction of a renal disease, further epidemiological analyses are illustrated below. These applications are exemplified for the prediction of the risk for renal cancer, glomerular diseases, renal tu- bulo-interstitial diseases, acute kidney failure and/or chronic kidney disease, urolithiasis and/or other dis- orders of kidney and ureter. Similar results apply to the other renal diseases listed in Table 1. Results are shown for a multi-biomarker score combining the 25 bi- omarkers featured in Figures 1-10. Similar results, al- beit slightly weaker, are obtained with combinations of only two or three individual biomarkers.
Figure lla shows the increase in the risk for renal cancer (ICD-10 diagnosis code C64) along with in- creasing levels of a multi-biomarker score composed of the weighted sum of 25 biomarkers. On the left-hand side, the risk increase is plotted in the form of gra- dient percentile plots, showing the proportion of indi- viduals who developed renal cancer during follow-up when binning individuals into the percentiles of the bi- omarker levels. Each dot corresponds to approximately 500 individuals. In the Kaplan-Meier plots on the right- hand side, the cumulative risk for renal cancer during follow-up is illustrated for selected quantiles of the multi-biomarker score. Both plots serve to demonstrate that the risk is increasing non-linearly in the high end of the distribution of the multi-biomarker score. The plots are shown for the validation set part of the study & population, i.e. 50% which was not included for deriva-
N tion of the multi-biomarker scores (n = 63 597 individ- 3 30 uals). 2 Figure 11b shows the hazard ratio of the same
Ek multi-biomarker score with the future onset of renal + cancer (ICD-10 diagnosis code C64) when accounting for > relevant risk factor characteristics of the study par- = 35 ticipants. The first two panels demonstrat that the risk
S prediction works effectively for both men and women and for people at different ages at the time of blood sampling. The last panel shows that the magnitude of the hazard ratio is only modestly attenuated when accounting for body mass index and smoking status in the statisti- cal modelling.
Figure 12a shows the increase in the risk for glomerular diseases (ICD-10 subchapter N00-N08) along with increasing levels of a multi-biomarker score com- posed of the weighted sum of 25 biomarkers. On the left- hand side, the risk increase is plotted in the form of gradient percentile plots, showing the proportion of individuals who developed glomerular diseases during follow-up when binning individuals into the percentiles of the biomarker levels. Each dot corresponds to ap- proximately 500 individuals. In the Kaplan-Meier plots on the right-hand side, the cumulative risk for glomer- ular diseases during follow-up is illustrated for se- lected quantiles of the multi-biomarker score. Both plots serve to demonstrate that the risk is increasing non-linearly in the high end of the distribution of the multi-biomarker score. The plots are shown for the val- idation set part of the study population, i.e. 50% which was not included for derivation of the multi-biomarker scores (n = 63 510 individuals).
Figure 12b shows the hazard ratio of the same multi-biomarker score with the future onset of glomer- ular diseases (ICD-10 subchapter NO0-N08) when account- ing for relevant risk factor characteristics of the & study participants. The first two panels demonstrate
N that the risk prediction works effectively for both men 3 30 and women, and for people at different ages at the time 2 of blood sampling. The last panel shows that the magni-
Ek tude of the hazard ratio is only modestly attenuated * when accounting for body mass index and smoking status > in the statistical modelling. = 35 Figure 13a shows the increase in the risk for
S renal tubulo-interstitial diseases (ICD-10 subchapter
N10-N16) along with increasing levels of a multi-
biomarker score composed of the weighted sum of 25 bi- omarkers. On the left-hand side, the risk increase is plotted in the form of gradient percentile plots, show- ing the proportion of individuals who developed renal tubulo-interstitial diseases during follow-up when bin- ning individuals into the percentiles of the biomarker levels. Each dot corresponds to approximately 500 indi- viduals. In the Kaplan-Meier plots on the right-hand side, the cumulative risk for renal tubulo-interstitial diseases during follow-up is illustrated for selected quantiles of the multi-biomarker score. Both plots serve to demonstrate that the risk is increasing non-linearly in the high end of the distribution of the multi-bi- omarker score. The plots are shown for the validation set part of the study population, i.e. 50% which was not included for derivation of the multi-biomarker scores (n = 63 352 individuals).
Figure 13b shows the hazard ratio of the same multi-biomarker score with the future onset of renal tubulo-interstitial diseases (ICD-10 subchapter N10-
N16) when accounting for relevant risk factor charac- teristics of the study participants. The first panel demonstrates that the risk prediction works effectively for both men and women. The second panel shows that risk prediction works effectively also for people at differ- ent ages at the time of blood sampling, with stronger results for young individuals. The last panel shows that & the magnitude of the hazard ratio is only modestly at-
N tenuated when accounting for body mass index and smoking 3 30 status in the statistical modelling. 2 Figure 14a shows the increase in the risk for =E acute kidney failure and chronic kidney disease (ICD-10 + subchapter N17-N19) along with increasing levels of a > multi-biomarker score composed of the weighted sum of = 35 25 biomarkers. On the left-hand side, the risk increase
S is plotted in the form of gradient percentile plots, showing the proportion of individuals who developed acute kidney failure or chronic kidney disease during follow-up when binning individuals into the percentiles of the biomarker levels. Each dot corresponds to ap- proximately 500 individuals. In the Kaplan-Meier plots on the right-hand side, the cumulative risk for acute kidney failure and chronic kidney disease during follow- up is illustrated for selected quantiles of the multi- biomarker score. Both plots serve to demonstrate that the risk is increasing non-linearly in the high end of the distribution of the multi-biomarker score. The plots are shown for the validation set part of the study pop- ulation, i.e. 50% which was not included for derivation of the multi-biomarker scores (n = 63 369 individuals).
Figure 14b shows the hazard ratio of the same multi-biomarker score with the future onset of acute kidney failure and chronic kidney disease (ICD-10 sub- chapter N17-N19) when accounting for relevant risk fac- tor characteristics of the study participants. The first panel demonstrates that the risk prediction works ef- fectively for both men and women. The second panel shows that risk prediction works effectively also for people at different ages at the time of blood sampling, with stronger results for young individuals. The last panel shows that the magnitude of the hazard ratio is only modestly attenuated when accounting for body mass index and smoking status in the statistical modelling.
Figure 15a shows the increase in the risk for
N urolithiasis (ICD-10 subchapter N20-N23) along with in-
N creasing levels of a multi-biomarker score composed of 3 30 the weighted sum of 25 biomarkers. On the left-hand 2 side, the risk increase is plotted in the form of gra- =E dient percentile plots, showing the proportion of indi- * viduals who developed urolithiasis during follow-up when > binning individuals into the percentiles of the bi- = 35 omarker levels. Each dot corresponds to approximately
S 500 individuals. In the Kaplan-Meier plots on the right- hand side, the cumulative risk for urolithiasis during follow-up is illustrated for selected quantiles of the multi-biomarker score. Both plots serve to demonstrate that the risk is increasing non-linearly in the high end of the distribution of the multi-biomarker score. The plots are shown for the validation set part of the study population, i.e. 50% which was not included for deriva- tion of the multi-biomarker scores (n = 62 967 individ- uals).
Figure 15b shows the hazard ratio of the same multi-biomarker score with the future onset of urolithi- asis (ICD-10 subchapter N20-N23) when accounting for relevant risk factor characteristics of the study par- ticipants. The first panel demonstrates that the risk prediction works effectively for both men and women. The second panel shows that risk prediction works effec- tively also for people at different ages at the time of blood sampling, with stronger results for young indi- viduals. The last panel shows that the magnitude of the hazard ratio is only modestly attenuated when accounting for body mass index and smoking status in the statisti- cal modelling.
Figure 16a shows the increase in the risk for other disorders of kidney and ureter (ICD-10 subchapter
N25-N29) along with increasing levels of a multi-bi- omarker score composed of the weighted sum of 25 bi- omarkers. On the left-hand side, the risk increase is plotted in the form of gradient percentile plots, show-
N ing the proportion of individuals who developed other
N disorders of kidney and ureter during follow-up when 3 30 binning individuals into the percentiles of the bi- 2 omarker levels. Each dot corresponds to approximately
Ek 500 individuals. In the Kaplan-Meier plots on the right- * hand side, the cumulative risk for other disorders of > kidney and ureter during follow-up is illustrated for = 35 selected quantiles of the multi-biomarker score. Both
S plots serve to demonstrate that the risk is increasing non-linearly in the high end of the distribution of the multi-biomarker score. The plots are shown for the val- idation set part of the study population, i.e. 50% which was not included for derivation of the multi-biomarker scores (n = 63 475 individuals).
Figure 16b shows the hazard ratio of the same multi-biomarker score with the future onset of other disorders of kidney and ureter (ICD-10 subchapter N25-
N29) when accounting for relevant risk factor charac- teristics of the study participants. The first panel demonstrates that the risk prediction works effectively for both men and women. The second panel shows that risk prediction works effectively also for people at differ- ent ages at the time of blood sampling, with stronger results for young individuals. The last panel shows that the magnitude of the hazard ratio is only modestly at- tenuated when accounting for body mass index and smoking status in the statistical modelling.
It is obvious to a person skilled in the art that with the advancement of technology, the basic idea may be implemented in various ways. The embodiments are thus not limited to the examples described above; instead they may vary within the scope of the claims.
The embodiments described hereinbefore may be used in any combination with each other. Several of the embodiments may be combined together to form a further & embodiment. A method disclosed herein may comprise at
N least one of the embodiments described hereinbefore. It 3 30 will be understood that the benefits and advantages 2 described above may relate to one embodiment or may
Ek relate to several embodiments. The embodiments are not * limited to those that solve any or all of the stated > problems or those that have any or all of the stated = 35 benefits and advantages. It will further be understood
S that reference to 'an' item refers to one or more of those items. The term “comprising” is used in this specification to mean including the feature(s) or act (s) followed thereafter, without excluding the presence of one or more additional features or acts.
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Claims (9)

1. A method for determining whether a subject is at risk of developing a renal disease; wherein the method comprises determining in a biological sample obtained from the subject a quantitative value of at least one biomarker of the following in the biological sample: - glycoprotein acetyls, - a ratio of docosahexaenoic acid to total fatty acids, - a ratio of linoleic acid to total fatty acids, - a ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, - a ratio of omega-3 fatty acids to total fatty acids, - a ratio of omega-6 fatty acids to total fatty acids, - a ratio of saturated fatty acids to total fatty acids, - fatty acid degree of unsaturation, - docosahexaenoic acid, - linoleic acid, - monounsaturated fatty acids and/or oleic acid, - omega-3 fatty acids, - omega-6 fatty acids, & - triglycerides in high-density lipoprotein N (HDL), 3 30 - triglycerides in low-density lipoprotein x (LDL), =E - high-density lipoprotein (HDL) particle + size, > - low-density lipoprotein (LDL) particle size, = 35 - very-low-density lipoprotein (VLDL) particle N size, - acetate,
- citrate, - pyruvate, - alanine, - glutamine, - histidine, - isoleucine, - phenylalanine; and comparing the quantitative value(s) of the at least one biomarker to a control sample or to a control value; wherein an increase or a decrease in the quantitative value(s) of the at least one biomarker, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing the renal disease; wherein the method comprises determining in the biological sample obtained from the subject a quantitative value of the following biomarkers: - glycoprotein acetyls, - at least one fatty acid measure(s) of the following: the ratio of docosahexaenoic acid to total fatty acids, docosahexaenoic acid, the ratio of linoleic acid to total fatty acids, linoleic acid, the ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, the ratio of omega-6 fatty acids to total fatty acids, omega-6 fatty acids, fatty acid degree of unsaturation; N wherein the guantitative values of the N biomarkers are measured using nuclear magnetic resonance 3 30 spectroscopy; and 2 wherein the renal disease comprises or is renal =E cancer, glomerular disease, renal tubulo-interstitial * disease, acute kidney failure, chronic kidney disease, > hypertensive chronic kidney disease, and/or = 35 urolithiasis.
S
2. The method according to claim 1, wherein the method comprises determining in the biological sample quantitative values of a plurality of biomarkers, such as two, three, four, five or more biomarkers.
3. The method according to claim 1 or 2, wherein the renal disease comprises or is glomerular disease (ICD-10 diagnosis code N00-N08); renal tubulo interstitial disease (ICD-10 diagnosis code N10-N16); acute kidney failure and/or chronic kidney disease (ICD- diagnosis code N17-N19); and/or urolithiasis (ICD-10 diagnosis code N20-N23). 10
4. The method according to any one of claims 1 - 3, wherein the renal disease comprises or is malignant neoplasm of kidney, except renal pelvis (ICD- 10 diagnosis code C64); hypertensive chronic kidney disease (ICD-10 diagnosis code I12); chronic nephritic syndrome (ICD-10 diagnosis code N03); glomerular disorder in a disease classified elsewhere (ICD-10 diagnosis code N08); tubulo-interstitial nephritis, not specified as acute or chronic (ICD-10 diagnosis code N12); obstructive and/or reflux uropathy (ICD-10 diagnosis code N13); acute kidney failure (ICD-10 diagnosis code N17); chronic kidney disease (ckd) (ICD- 10 diagnosis code N18); unspecified kidney failure (ICD- 10 diagnosis code N19); calculus of kidney and/or ureter (ICD-10 diagnosis code N20); calculus of lower urinary tract (ICD-10 diagnosis code N21); and/or unspecified renal colic (ICD-10 diagnosis code N23).
5. The method according to any one of claims 1 N - 4, wherein the renal disease comprises or is N hypertensive CKD with stage 5 CKD or end stage renal 3 30 disease (ICD-10 diagnosis code I12.0); chronic nephritic 2 syndrome with unspecified morphologic changes (ICD-10 =E diagnosis code N03.9); hydronephrosis with renal and + ureteral calculous obstruction (ICD-10 diagnosis code > N13.2); other and/or unspecified hydronephrosis (ICD-10 = 35 diagnosis code N13.3); acute kidney failure, unspecified S (ICD-10 diagnosis code N17.9); chronic kidney disease, stage 2 (mild) (ICD-10 diagnosis code N18.2); chronic kidney disease, stage 3 (moderate) (ICD-10 diagnosis code N18.3); chronic kidney disease, stage 4 (severe) (ICD-10 diagnosis code N18.4); chronic kidney disease, stage 5 (ICD-10 diagnosis code N18.5); chronic kidney disease, unspecified (ICD-10 diagnosis code N18.9); calculus of kidney (ICD-10 diagnosis code N20.0); calculus of ureter (ICD-10 diagnosis code N20.1); calculus in bladder (ICD-10 diagnosis code N21.0); and/or cyst of kidney, acauired (ICD-10 diagnosis code
N28.1).
6. The method according to any one of claims 1 = 5, wherein the method further comprises determining whether the subject is at risk of developing the renal disease using a risk score, hazard ratio, odds ratio, and/or predicted absolute risk or relative risk calculated on the basis of the auantitative value(s) of the at least one biomarker or of the plurality of the biomarkers.
7. The method according to any one of claims 1 — 6, wherein the risk score, hazard ratio, odds ratio, and/or predicted relative risk and/or absolute risk may be calculated on the basis of at least one further meas- ure, such as a characteristic of the subject.
8. The method according to claim 7, wherein the characteristic of the subject includes one or more of age, height, weight, body mass index, race or ethnic group, smoking, and/or family history of renal diseases; & or wherein the characteristic of the subject comprises N age, sex, smoking status, use of alcohol, blood pres- 3 30 sure, abnormalities in kidney structure, genetic risk 2 and/or prior medical and/or family history of having =E renal diseases and/or other comorbidities, such as di- + abetes or cardiovascular diseases; or wherein the char- > acteristic of the subject comprises other analyses and = 35 tests, such as an imaging test to assess kidneys’ struc- S ture and size, a blood test for protein biomarkers such as C-rective protein (CRP) or cystatin C or C-peptide,
blood test for creatinine and/or cystatin C to compute estimated glomerular filtration rate (eGFR), cystatin C estimated glomerular filtration rate (eGFR-cys) and/or creatinine-cystatin C estimated glomerular filtration rate (eGFRcr-cys) and/or test for the level of urine albumin and/or urine albumin to creatinine ratio (UACR).
9. The method according to any one of claims 1 - 8, wherein the method comprises determining in the biological sample obtained from the subject a quantita- tive value of the following biomarkers: - glycoprotein acetyls, - the ratio of docosahexaenoic acid to total fatty acids, - the ratio of linoleic acid to total fatty acids, - the ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, - the ratio of omega-3 fatty acids to total fatty acids, - the ratio of omega-6 fatty acids to total fatty acids, - the ratio of saturated fatty acids to total fatty acids, cn - fatty acid degree of unsaturation, O N 25 - docosahexaenoic acid, 3 e - linoleic acid, oO = - monounsaturated fatty acids and/or oleic > acid, o © o - omega-3 fatty acids, N S 30 - omega-6 fatty acids,
- triglycerides in high-density lipoprotein (HDL), - triglycerides in low-density lipoprotein (LDL), - high-density lipoprotein (HDL) particle size, - low-density lipoprotein (LDL) particle size, - very-low-density lipoprotein (VLDL) particle size, - acetate, - citrate, - pyruvate, - alanine, - glutamine, - histidine, - isoleucine, - phenylalanine; and comparing the quantitative value(s) of the biomarkers to a control sample or to a control value(s); wherein an increase or a decrease in the guantitative value(s) of the biomarkers, when compared S to the control sample or to the control value, is/are O N indicative of the subject having an increased risk of 3 developing the renal disease. 2 25 I jami a o © o N O N
FI20215180A 2021-02-19 2021-02-19 Method for determining whether a subject is at risk of developing a renal disease FI130199B (en)

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