WO2017161256A1

WO2017161256A1 - Systems and methods for determining likelihood of alzheimer's disease and/or mild cognitive impairment status in a patient

Info

Publication number: WO2017161256A1
Application number: PCT/US2017/022947
Authority: WO
Inventors: Eugenia Wang
Original assignee: Advanced Genomic Technology, Llc
Priority date: 2016-03-18
Filing date: 2017-03-17
Publication date: 2017-09-21

Abstract

The disclosure relates to systems and methods for assessing and determining the likelihood or prognosis of Alzheimer's disease or mild cognitive impairment in a subject.

Description

SYSTEMS AND METHODS FOR DETERMINING LIKELIHOOD OF ALZHEIMER'S DISEASE AND/OR MILD COGNITIVE IMPAIRMENT STATUS IN A PATIENT

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. provisional patent application serial number 62/310,103 filed on March 18, 2016 in the name of Eugenia Wang. The contents of the above-referenced document are incorporated herein by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Part of the present disclosure was made with government support under grant No. KSTC- 184-512-15- 208, from the Kentucky Cabinet for Economic Development, Office of Entrepreneurship. Part of the present disclosure was made with government support under grant No. 1R44AG044157 awarded by the National Institute on Aging, National Institutes of Health. The U.S. Government has certain rights in parts of the invention.

FIELD OF TECHNOLOGY

The present disclosure generally relates to systems and methods for assessing and determining the likelihood or prognosis of Alzheimer's disease and/or mild cognitive impairment in a subject.

BACKGROUND INFORMATION

The importance of early diagnosis, treatment and prevention of Alzheimer's disease (AD) attracts the attention of scientific and medical communities, regulatory agencies, such as the US Food and Drug Administration (FDA), and industry and government leaders in many countries. The number of AD patients and those in high risk populations grows quickly, especially in developed countries, due to increased lifespan. A number of investigational anti-AD drugs, targeting various processes characteristic of AD pathogenesis, have failed in recent clinical trials (Gerald et al., Alzheimer's disease market: hope deferred. Nat Rev Drug Discov. 2013; 12: 19-20), likely due to massive neuronal loss and advanced stages of the disease in the enrolled patients.

It has been demonstrated that AD dementia is preceded by 20-30 years of the disease development, initially without clinical symptoms (pre -symptomatic AD), and then manifested as mild cognitive impairment (MCI) (e.g., Weiner et al., Alzheimer's Disease Neuroimaging Initiative. The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer's Dement. 2013 Aug 6; Pillai et al., Clinical trials in predementia stages of Alzheimer disease. Med Clin North Am. 2013; 97:439-457; Sperling et al., Testing the right target and right drug at the right stage. Sci. Transl. Med. 2011 ; 3 11 lcm33.) It is important to note that the detailed analysis of failed clinical trials has demonstrated a therapeutic benefit in the sub-groups of patients with mild and moderate symptoms of AD (Cunningham et al., Drug development in dementia. Maturitas, 2013 Nov;76(3):260-6).

The high need for development of new methods for early AD detection is also emphasized in recent publications from the FDA (Delbeke et al, FDA AD Drug Development Guidance. J Nucl. Med. 2013;54: 16N and the U.S. Department of Health and Human Services ("National Alzheimer's Project Act"). Since cognitive testing cannot easily identify MCI patients definitively with symptomatic manifestation stages of bona fide AD phase, and most importantly those individuals, At-Risk for Alzheimer's disease (ARAD) without any detectable cognitive deficits, yet at high probability developing dementia later; thus effective systems and methods are necessary for not only successful patient stratification and treatment monitoring but also preventive medicine for dementia to slow down disease onset and/or progression.

Due to the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the US and similar projects in other countries, a significant progress in early detection of AD with high sensitivity and specificity by imaging techniques and analysis of protein biomarkers in cerebrospinal fluid has been achieved. However, the high cost and invasiveness of these methods make their application for primary screening of large populations impractical.

Various approaches to the development of non-invasive or minimally invasive assays for early detection of AD have been tested.

U.S. Patent Application Publication No. 2013/0,040,303, incorporated by reference herein in its entirety, describes systems and methods which makes use of circulating biomarkers in substantially cell free biological sample, such as serum, urine, saliva or plasma, to determine the likelihood of the presence of Alzheimer's disease (AD) in an individual, to assess and determine the various stages of progression of AD in an individual, or determine the presence of MCI in an individual in comparison to a normal elderly control (NEC) individual. These biomarkers. however, do not appear to distinguish with high sensitivity, specificity and overall accuracy between AD and MCI or NEC. These biomarkers, however, do not appear to distinguish the AD versus MCI stages; nor distinguish the asymptomatic at-risk phase for younger individuals with high probability to develop signs of cognitive deficits later in life.

U.S. Patent Application Publication No. 2014/0378439, incorporated by reference herein in its entirety, describes methods which makes use of circulating biomarkers in substantially cell free biological sample, such as serum, urine or plasma, to determine the likelihood of the presence of AD in an individual. These biomarkers, however, do not appear to distinguish with high sensitivity, specificity and overall accuracy to distinguish the MCI from those advancing to the first phase of bona fide AD dementia, i.e. the very first stage, mild stage, when patient starting to lose their capability to perform their daily function.

SUMMARY OF DISCLOSURE

The present specification aims to provide advanced systems and methods that alleviate at least in part some of the deficiencies of the existing methods and systems.

As embodied and broadly described herein the invention provides a method for determining an Alzheimer's disease (AD) or mild cognitive impairment (MCI) likelihood status in a subject, the method comprising obtaining a substantially cell free biological sample from the subject, measuring an expression level of circulating microRNA miR-411 in the sample, and comparing the expression level of the circulating microRNA with a reference level of microRNA miR-411 to establish the likelihood of AD or MCI status in the subject. In one embodiment, this reference level is derived from AD, or MCI, or normal elderly control (NEC) reference subjects.

In one non-limiting embodiment, the expression level of circulating microRNA miR-411 in the herein described method is used to distinguish between a likelihood of MCI and severe or moderate or mild AD.

As embodied and broadly described herein the invention provides a method for determining an Alzheimer's disease (AD) likelihood status in a subject, the method comprising obtaining a substantially cell free biological sample from the subject, measuring an expression level of circulating microRNA miR- 181c in the sample, and comparing the expression level of the circulating microRNA with a reference level of microRNA miR-181c to establish the likelihood of AD in the subject. In one embodiment, this reference level is derived from AD, or mild cognitive impairment (MCI), or normal elderly control (NEC) reference subjects.

In one non-limiting embodiment, the expression level of circulating microRNA miR-181c in the herein described method is used to distinguish between a likelihood of severe or moderate AD and MCI or NEC.

As embodied and broadly described herein the invention provides a method for assisting in prognosis of late-life Alzheimer's disease (AD) in a subject, comprising obtaining a substantially cell free biological sample of the subject, the subject being aged in the range of 40 to 69 years of age, measuring an expression level of circulating microRNA miR-34c or miR-34a in the sample, and comparing said expression level with a reference level of the microRNA to establish the prognosis likelihood of late-life AD of the subject. In one embodiment, this reference level is derived from high body mass index (BMI) or high waist circumference (WC) reference subjects being aged in the range of 40 to 69 years of age. As embodied and broadly described herein the invention provides a method for assisting in prognosis of late-life Alzheimer's disease (AD) likelihood in a subject, comprising obtaining a substantially cell free biological sample of the subject, the subject being aged in the range of 40 to 69 years of age, measuring an expression level of circulating microRNA miR-27a in the sample, and comparing said expression level with a reference level of the microRNA to establish the prognosis likelihood of late-life AD of the subject. In one embodiment, this reference level is derived from family history (FH) positive or FH negative reference subjects.

In one non-limiting embodiment, the above subject tested to obtain the prognosis can be in a mild cognitive impaired phase with semantic deficit while retaining capability to perform daily function or at an asymptomatic at-risk phase.

As embodied and broadly described herein the invention provides a method for evaluating a subject suspected with Alzheimer's disease (AD) or mild cognitive impairment (MCI), comprising obtaining a substantially cell free biological sample of the subject, measuring an expression level of circulating microRNA miR-411 in the sample, comparing the expression level of the microRNA to a threshold reference level of the microRNA derived from AD, or MCI, or normal elderly control (NEC) reference subjects.

As embodied and broadly described herein the invention provides a method for evaluating a subject suspected with Alzheimer's disease (AD), comprising obtaining a substantially cell free biological sample of the subject, measuring an expression level of circulating microRNA miR-181c in the sample, comparing the expression level of the microRNA to a threshold reference level of the microRNA derived from AD, or MCI, or normal elderly control (NEC) reference subjects.

In one embodiment, the herein described method for evaluating a subject suspected with Alzheimer's disease (AD) or mild cognitive impairment (MCI) further comprises additionally measuring an expression level of circulating microRNA miR-34c and/or miR-34a in a substantially cell free biological sample obtained from the subject, and comparing the expression level of circulating microRNA miR-34c and/or miR-34a to a threshold reference level of the microRNA derived from AD, or MCI, or normal elderly control (NEC) reference subjects. In one non-limiting embodiment, the biological sample in which the expression level of circulating microRNA miR-34c and/or miR-34a is measured is the same biological sample in which the expression level of circulating microRNA miR-411 and/or miR-181c is measured.

As embodied and broadly described herein the invention provides a system, comprising: a reverse transcription real time polymerase chain reaction (RT-qPCR) apparatus for processing a substantially cell free biological sample from a subject having a family history of AD to obtain an amplification of circulating microRNA miR-27a, wherein said RT-qPCR apparatus is configured for generating a first signal indicative of the amplification of the predefined circulating microRNA; and an apparatus having a first input in communication with said RT-qPCR apparatus for receiving said first signal; a second input for receiving a second signal derived from user generated data; a processing unit; a memory; and an output; said processing unit being programmed for: processing the first signal to derive an expression level of the circulating microRNA in the sample; processing the second signal to derive a risk likelihood value; comparing the expression level of the circulating microRNA in the sample to a reference level stored in the memory; and causing an output signal to be released via the output, the output signal being indicative of an at-risk of Alzheimer's disease (ARAD) likelihood status of the subject and being derived at least in part by processing the outcome of said comparison and said risk likelihood value.

As embodied and broadly described herein the invention provides a system, comprising: a reverse transcription real time polymerase chain reaction (RT-qPCR) apparatus for processing a substantially cell free biological sample from a subject to obtain an amplification of circulating microRNA miR-411, wherein said RT-qPCR apparatus is configured for generating a signal indicative of the amplification of the circulating microRNA; and an apparatus having an input in communication with said RT-qPCR apparatus for receiving said signal; a processing unit; a memory; and an output; said processing unit being programmed for: processing the signal to derive an expression level of the circulating microRNA in the sample; comparing the expression level of the microRNA in the sample to a reference level of microRNA stored in the memory; and causing an output signal to be released via the output, the output signal being indicative of an Alzheimer's disease (AD) or mild cognitive impairment (MCI) or normal elderly control (NEC) likelihood status of the subject at least being based on an outcome of said comparison.

As embodied and broadly described herein the invention provides a system, comprising: a reverse transcription real time polymerase chain reaction (RT-qPCR) apparatus for processing a substantially cell free biological sample from a test subject to obtain an amplification of circulating microRNA miR-181c, wherein said RT-qPCR apparatus is configured for generating a signal indicative of the amplification of the circulating microRNA; and an apparatus having an input in communication with said RT-qPCR apparatus for receiving said signal; a processing unit; a memory; and an output; said processing unit being programmed for: processing the signal to derive an expression level of the circulating microRNA in the sample; comparing the expression level of the microRNA in the sample to a reference level of microRNA stored in the memory; and causing an output signal to be released via the output, the output signal being indicative of an Alzheimer's disease (AD) likelihood status of the subject at least being based on an outcome of said comparison. As embodied and broadly described herein the invention provides a computer program product comprising one or more tangible non-transitory computer readable storage media storing computer executable instructions for monitoring a subject over a data network, the computer executable instructions, when executed, cause a programmable system including at least one programmable processor to perform operations, the operations comprising: receiving data over the data network from one or more computing devices interconnected with the programmable system over the data network, the received data conveying information on an expression level of circulating microR A miR-27a in a substantially cell free biological sample of the subject, and on Alzheimer's disease (AD) family history of the subject; at the programmable system, processing the data conveying the expression level of microRNA miR-27a in a substantially cell free sample of the subject to derive a criticality level for the subject being monitored, the criticality level being derived at least in part by processing the expression level of microRNA miR-27a and the AD family history (FH) information; and selectively transmitting electronic notification data over the data network in connection with the subject following a criticality level associated with the subject exceeding a threshold criticality level, the electronic notification data being transmitted to a computing device.

As embodied and broadly described herein the invention provides a computer program product comprising one or more tangible non-transitory computer readable storage media storing computer executable instructions for monitoring a subject over a data network, the computer executable instructions, when executed, cause a programmable system including at least one programmable processor to perform operations, the operations comprising: receiving data over the data network from one or more computing devices interconnected with the programmable system over the data network, the received data conveying information on an expression level of circulating microRNA miR-34c or miR- 34a in a substantially cell free biological sample of the subject, and on Alzheimer's disease (AD) family history (FH) of the subject; at the programmable system, processing the data conveying the expression level to derive a criticality level for the patient being monitored, the criticality level being derived at least in part by processing the expression level and the AD FH information; and selectively transmitting electronic notification data over the data network in connection with the subject following a criticality level associated with the subject exceeding a threshold criticality level, the electronic notification data being transmitted to a computing device.

As embodied and broadly described herein the invention provides a computer program product comprising one or more tangible non-transitory computer readable storage media storing computer executable instructions for monitoring a subject over a data network, the computer executable instructions, when executed, cause a programmable system including at least one programmable processor and a memory to perform operations, the operations comprising: receiving data over the data network from one or more computing devices interconnected with the programmable system over the data network, the received data conveying information on an expression level of circulating microRNA miR- 411 in a substantially cell free biological sample of the subject; at the programmable system, processing the data conveying the circulating level of microRNA miR-411 in a substantially cell free sample of the subject to derive a criticality level for the subject being monitored, the criticality level being derived at least in part by comparing the expression level of circulating microRNA miR-411 to a reference level stored in said memory; and selectively transmitting electronic notification data over the data network in connection with the subject following a criticality level associated with the subject exceeding a threshold criticality level, the electronic notification data being transmitted to a computing device.

As embodied and broadly described herein the invention provides a computer program product comprising one or more tangible non-transitory computer readable storage media storing computer executable instructions for monitoring a subject over a data network, the computer executable instructions, when executed, cause a programmable system including at least one programmable processor and a memory to perform operations, the operations comprising: receiving data over the data network from one or more computing devices interconnected with the programmable system over the data network, the received data conveying information on an expression level of circulating microRNA miR- 181c in a substantially cell free biological sample of the subject; at the programmable system, processing the data conveying the circulating level of microRNA miR-181c in a substantially cell free sample of the subject to derive a criticality level for the patient being monitored, the criticality level being derived at least in part by comparing the expression level of circulating microRNA miR-181c to a reference level stored in said memory; and selectively transmitting electronic notification data over the data network in connection with the subject following a criticality level associated with the subject exceeding a threshold criticality level, the electronic notification data being transmitted to a computing device.

In some specific practical implementations, the computing device may include a smartphone, a tablet, a general purpose computer and/or any other suitable computing device and the electronic notification data may convey an e-mail message, an SMS message and/or or any other suitable electronic message.

Suitable networks for use with the present system include any of a wide variety of physical infrastructures, protocols, connections, and encryption algorithms. According to various embodiments, suitable networking practices may be implemented in order to comply with accepted healthcare standards and/or government regulations, such as for example practices for ensuring confidentialit of patient information. According to some specific implementations, the electronic notification data is configured for causing a graphical user interface (GUI) to be displayed on a display screen of the computing device associated with a particular medical expert or with the user, the GUI including AD likelihood information elements associated with the particular subject. The GUI may provide the medical expert / subject with one or more user operable control components to enable the user to perform different functions such as, for example, requesting additional information associated with the particular subject and/or establishing a communication with a computing device located in proximity to the clinical module. In some practical implementations, the communication established with the computing device located in proximity to the clinical module may be a telephone call, a video call, an e-mail, an SMS message, an audio alarm trigger, a visual alarm trigger or any other suitable form of communication.

As embodied and broadly described herein the invention provides a method for assisting in prognosis of late-life Alzheimer's disease (AD) in a subject, comprising obtaining a substantially cell free biological sample from the subject; measuring an expression level of circulating microRNA miR-27a in said sample; and comparing said expression level to a reference level derived from reference subjects having a positive AD family history.

As embodied and broadly described herein the invention provides a method for assisting in prognosis of late-life Alzheimer's disease (AD) in a subject, comprising obtaining a substantially cell free biological sample from the subject; measuring an expression level of circulating microRNA miR-34c or miR-34a in said sample; and comparing said expression level to a reference level derived from at-risk of AD (ARAD) having high body mass index or high waist circumference reference subjects.

In one practical embodiment, the herein described "reference level" may be a reference level or reference level range derived from a respective cohort of at least 10 subjects whose AD, MCI or NEC likelihood status and/or AD family history and/or body mass index (BMI) or waist circumference (WC) is known. For example, the AD, MCI or NEC status can be known by assessing the subject's Mini Mental State Evaluation (MMSE) score, or Montreal Cognitive Assessment (MoCA) score, or any other clinical assessment approach known to the person of skill in the art. In another embodiment, the reference level or reference level range may be derived from a respective cohort of at least 15 subjects, or at least 20 subjects, or at least 25 subjects, or at least 30 subjects, or more (e.g., at least 100 subjects), whose AD, MCI or NEC status and/or AD family history and/or body mass index (BMI) or waist circumference (WC) is known.

In one practical embodiment, the herein described "measuring an expression level of a circulating microRNA" may be performed using reverse transcriptase real time polymerase chain reaction (RT- qPCR). In one practical embodiment, the herein described "substantially cell free biological sample" includes urine, saliva, plasma or serum.

In one practical embodiment, the herein described "substantially cell free biological sample" includes a plasma or serum sample.

All features of embodiments which are described in this disclosure and are not mutually exclusive can be combined with one another. Elements of one embodiment can be utilized in the other embodiments without further mention. Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying Figures.

BRIEF DESCRIPTION OF THE FIGURES

A detailed description of specific embodiments is provided herein below with reference to the accompanying drawings in which:

Fig. 1A illustrates a graph showing the relative expression level of microRNA miR-299 in plasma samples of FH-positive cohort (n=22) and of FH-negative cohort (n=19).

Fig. IB illustrates a graph showing Receiver Operating Characteristic (ROC) curves for the results shown in 1A.

Fig. 2A illustrates a graph showing the relative expression level of microRNA miR-21 in plasma samples of FH-positive cohort (n=23) and of FH-negative cohort (n=22).

Fig. 2B illustrates a graph showing ROC curves for the results shown in Fig. 2A.

Fig. 3A illustrates a graph showing the relative expression level of microRNA let-7d in plasma samples of FH-positive cohort (n=22) and of FH-negative cohort (n=23).

Fig. 3B illustrates a graph showing ROC curves for the results shown in Fig. 3A.

Fig. 4A illustrates a graph showing the relative expression level of microRNA miR-27a in plasma samples of FH-positive cohort (n=17) and of FH-negative cohort (n=17).

Fig. 4B illustrates a graph showing ROC curves for the results shown in Fig. 4A.

Fig. 5A illustrates a graph showing the relative expression level of microRNA miR-133a in plasma samples of FH-positive cohort (n=22) and of FH-negative cohort (n=22).

Fig. 5B illustrates a graph showing the relative expression level of microRNA miR-27b in plasma samples of FH-positive cohort (n=18) and of FH-negative cohort (n=17). Fig. 5C illustrates a graph showing the relative expression level of microRNA let-7f in plasma samples of FH-positive cohort (n=22) and of FH-negative cohort (n=21).

Fig. 6A illustrates a graph showing the relative expression level of microRNA miR-34c in plasma samples of severe/moderate Alzheimer's disease (AD) cohort (n=14), mild AD cohort (n=15), mild cognitive impairment (MCI) cohort (n=26) and normal elderly control (NEC) cohort (n=28).

Figs. 6B, 6C, 6D and 6E illustrate graphs showing ROC curves for the results shown in Fig. 6A.

Fig. 7A illustrates a graph showing the relative expression level of microRNA miR-181c plasma samples of severe / moderate AD cohort (n=13), mild AD cohort (n=14), MCI cohort (n=27) and NEC cohort (n=26).

Figs. 7B, 7C, 7D and 7E illustrate graphs showing ROC curves for the results shown in Fig. 7A.

Fig. 8A illustrates a graph showing the relative expression level of circulating microRNA miR-411 in plasma samples of severe / moderate AD cohort (n=13), mild AD cohort (n=14), MCI cohort (n=27) and NEC cohort (n=28).

Figs. 8B, 8C, 8D and 8E illustrate graphs showing ROC curves for the results shown in Fig. 8A.

Figs. 9A and 9B illustrate tables which include information relating to family history of tested patients. Figure 9C illustrates a database infrastructure from patient recruitment to plasma sample collection and analysis.

Fig. 10A illustrates a graph showing the average Ct determined from control spiked-in Cel-54.

Fig. 10B illustrates a graph showing the relative expression levels of circulating microRNA miR-34a in plasma sample from FH+ cohort (n=7) and FH- cohort (n=7) identified in Fig. 9A, 9B and 9C, where FH+ represents FH-positive and obese subjects, whereas FH- represents FH-negative and normal BMI & waist circumference subjects.

Fig. IOC illustrates a graph showing the relative expression levels of circulating microRNA miR-34c in plasma sample from FH+ cohort (n=6) and FH- cohort (n=8) identified in Fig. 9A, 9B and 9C, where FH+ represents FH-positive and obese subjects, whereas FH- represents FH-negative and normal BMI & waist circumference subjects.

Figs. 11A illustrates a graph that shows the correlation between body mass index (BMI) values and qPCR values measuring the expression level of circulating microRNA miR-34a in plasma samples from FH+ (obese) and FH- cohorts . Fig. 1 IB illustrates a graph that shows the correlation between waist circumference size (cm) and qPCR values measuring the expression level of circulating microRNA miR-34a in plasma samples from FH+ (obese) and FH- cohorts.

Fig 12A illustrates a graph that shows the correlation between qPCR values measuring the expression level of circulating microRNA miR-34a and leptin levels (measured by ELISA) in plasma samples from FH+ (obese) cohorts and FH- cohorts.

Fig. 12B illustrates a graph that shows the correlation between qPCR values measuring the expression level of circulating microRNA miR-34a and glucose levels (measured by ELISA) in plasma samples from FH+ (obese) cohorts and FH- cohorts.

Fig. 13A illustrates a graph that shows the correlation between the waist circumference (in cm) and leptin levels in plasma samples from FH+ (obese) cohorts and FH- cohorts.

Fig. 13B illustrates a graph that shows the correlation between the BMI and leptin levels in plasma samples from FH+ (obese) cohorts and FH- cohorts.

Fig. 13C illustrates a graph that shows the correlation between waist circumference (in cm) and glucose levels in plasma samples from FH+ (obese) cohorts and FH- cohorts.

Fig. 13D illustrates a graph that shows the correlation between the BMI and glucose levels in plasma samples from FH+ (obese) cohorts and FH- cohorts.

Figs. 14A and 14B illustrate graphs showing ROC curves for the results shown in Fig. 13A, 13B, 13C and 13D.

Fig. 15A illustrates a graph showing the relative expression level of circulating microRNA miR-34c in plasma samples from FH+, high BMI/WC cohorts and from FH+, normal BMI WC cohorts.

Fig. 15B illustrates a graph showing the relative expression level of circulating microRNA miR-34c in plasma samples from FH-, high BMI WC cohorts and from FH-, normal BMI/WC cohorts.

Fig. 15C illustrates a graph showing the relative expression level of circulating microRNA miR-34c in plasma samples from FH+, high BMI/WC cohorts and from FH-_; high BMI/WC cohorts.

Fig. 15D illustrates a graph showing the relative expression level of circulating microRNA miR-34c in plasma samples from FH+, normal BMI/WC cohorts and from FH-, normal BMI/WC cohorts.

Fig. 15E illustrates a graph showing the relative expression level of circulating microRNA miR-34c in plasma samples from FH+, high BMI/WC cohorts and from FH-, normal BMI/WC cohorts. Fig. 15F illustrates a graph showing the relative expression level of circulating microRNA miR-34c in plasma samples from FH+, normal BMI/WC cohorts and from FH-, high BMI/WC cohorts.

Figs. 16A and 16B illustrate graphs showing ROC curves for the results shown in Figs. 15A - 15F.

Fig. 17A illustrates a graph showing the relative expression level of leptin in plasma samples from FH+, high BMIAVC cohorts and from FH+, normal BMI/WC cohorts.

Fig. 17B illustrates a graph showing the relative expression level of leptin in plasma samples from FH-, high BMIAVC cohorts and from FH-, normal BMIAVC cohorts.

Fig. 17C illustrates a graph showing the relative expression level of leptin in plasma samples from FH+, high BMIAVC cohorts and from FH- high BMIAVC cohorts.

Fig. 17D illustrates a graph showing the relative expression level of leptin in plasma samples from FH+, normal BMIAVC cohorts and from FH-, normal BMIAVC cohorts.

Fig. 17E illustrates a graph showing the relative expression level of leptin in plasma samples from FH+, high BMIAVC cohorts and from FH- normal BMIAVC cohorts.

Fig. 17F illustrates a graph showing the relative expression level of leptin in plasma samples from FH+, normal BMIAVC cohorts and from FH-, high BMIAVC cohorts.

Fig. 18A illustrates a graph showing the relative expression level glucose in plasma samples from FH+, high BMIAVC cohorts and from FH+, normal BMIAVC cohorts.

Fig. 18B illustrates a graph showing the relative expression level of glucose in plasma samples from FH-, high BMIAVC cohorts and from FH-, normal BMIAVC cohorts.

Fig. 18C illustrates a graph showing the relative expression level of glucose in plasma samples from FH+, high BMIAVC cohorts and from FH- high BMIAVC cohorts.

Fig. 18D illustrates a graph showing the relative expression level of glucose in plasma samples from FH+, normal BMIAVC cohorts and from FH-, normal BMIAVC cohorts.

Fig. 18E illustrates a graph showing the relative expression level of glucose in plasma samples from FH+, high BMIAVC cohorts and from FH- normal BMIAVC cohorts.

Fig. 18F illustrates a graph showing the relative expression level of glucose in plasma samples from FH+, normal BMIAVC cohorts and from FH-, high BMIAVC cohorts.

Fig. 19A illustrates a graph that shows the correlation between the BMI and the expression level of circulating microRNA miR-34c in plasma samples from Figs. 15A-15F. Fig. 19B illustrates a graph that shows the correlation between expression level of circulating microRNA miR-34c in plasma samples and the WC, where the plasma samples are those from Figs. 15A-15F.

Fig. 20A illustrates a graph that shows the correlation between Ale test (%) and the glucose concentration (mmol/L) in plasma sample.

Fig. 20B illustrates a graph that shows the correlation between Ale test (%) and the expression level of circulating microRNA miR-34c in a substantially cell free plasma sample.

Fig. 20C illustrates a graph that shows the correlation between glucose concentration (mmol/L) in plasma sample and the expression level of circulating microRNA miR-34c in a substantially cell free plasma sample.

Fig. 20D illustrates a graph that shows the correlation between triglycerides concentration (mmol L) in plasma sample and the expression level of circulating microRNA miR-34c in a substantially cell free plasma sample.

Fig. 20E illustrates a graph that shows the correlation between total cholesterol concentration (mmol/L) in plasma sample and the expression level of circulating microRNA miR-34c in a substantially cell free plasma sample.

Fig. 20F illustrates a graph that shows the correlation between homocysteine concentration (μιηοΙ/L) in plasma sample and the expression level of circulating microRNA miR-34c in a substantially cell free plasma sample.

Fig. 21 illustrates a proposed model of the continuum of Alzheimer's disease (AD) from pre-symptomatic At-Risk of AD Phase (ARAD), which can be influenced by FH and BMI/WC, to Mild Cognitive Impairment (MCI) and three stages of bona fide AD.

Fig. 22 illustrates a proposed model of shared oxidative stress signaling networks between obesity and Alzheimer's disease (AD), regulated by oxidative stress-associated microRNAs.

Fig. 23 is a block diagram showing a system in accordance with an embodiment of the present disclosure.

Fig. 24 is a block diagram showing a clinical module of the system of Fig. 23 in accordance with an embodiment of the present disclosure. .

Fig. 25 is a block diagram showing a computing device of the system of Fig. 23 in accordance with an embodiment of the present disclosure. . Fig. 26 shows a specific example of implementation of a graphical user interface (GUI) which can be caused to be displayed on a device of the type depicted in Fig. 25 in accordance with an embodiment of the present disclosure.

Fig. 27 is a block diagram of a process that may be implemented by the clinical module of Fig. 24 in accordance with an embodiment of the present disclosure.

Fig. 28A is a Table that sets forth genes with at least one target site for microR A 411 with gene symbol, gene name, number of target sites, and implicated pathway provided. Gene names were obtained from the HUGO Gene Nomenclature Committee webpage.

Fig. 28B is a continuation of the Table of Fig. 28A.

Fig. 28C is a continuation of the Table of Fig. 28B.

Fig. 28D is a continuation of the Table of Fig. 28C.

In the figures, non-limiting embodiments are illustrated by way of example. It is to be expressly understood that the description and drawings are only for the purpose of illustrating certain embodiments and are an aid for understanding. The scope of the claims should not be limited by the embodiments set forth in the present disclosure, but should be given the broadest interpretation consistent with the description as a whole.

DETAILED DESCRIPTION

The present invention aims to improve the prognosis/diagnostic of Alzheimer's disease (AD), by providing methods and systems that make use of the herein described biomarkers for determining the likelihood of AD risk at an early stage and/or the likelihood of AD onset and/or the likelihood of conversion from MCI to AD and or the likelihood of AD progression. These systems and methods may have particular utility at least to assist in, for example, developing tests, tools and assay to study AD /MCI and/or for initiating an early therapy against AD / MCI.

AD can be diagnosed with reasonable accuracy at the dementia stage. In fact, a recent evidence based medicine review of the literature by the American Academy of Neurology documented that clinicians were quite accurate when the clinical diagnoses were subsequently compared to neuropathological findings (Knopman, DeKosky et al. 2001). However, as one identifies the disease process at an earlier point in the clinical continuum, the precision of the diagnosis is reduced. An important challenge is to try to identify the process at the pre-dementia stage and enhance the specificity of the clinical diagnosis through the use of imaging and other biomarkers. This approach assumes an underlying cascade of pathological events that lend themselves to intervention (Jack. Knopman et al. 2010; Petersen 2010). Biochemical and neuroimaging biomarkers can provide a window on the underlying neurobiology, facilitating early identification and intervention.

Defined herein are biomarkers that, in addition of being associated with the onset of AD and/or with one or more stages of AD, are associated with obesity. The data presented herein, thus, suggest a combination of biomarkers and physiological conditions (such as obesity and family history of AD) which when present in a subject, provide information about the subject's likelihood of developing AD or the likelihood of the subject to advance to more severe states of AD.

The person of skill will readily realize that the present disclosure, thus, affords in some embodiments with substantially accurate, minimally-invasive systems and methods which may facilitate patient and family counseling, optimizing stratification of sub-groups for enrolment in clinical drug trials, interpreting treatment outcome measures, and the like. The person of skill will also readily realize that the present disclosure may, alternatively or additionally, be useful in at least affording methods for better addressing concurrent medical conditions which may preclude or confound cognitive and neuropsychological testing.

For example, the person of skill in view of the teachings of the present disclosure will be able to implement and monitor a process for determining the conditions of a subject suspected of, or being susceptible to, having AD to clinically assess and initiate measures to mitigate risks that the subject at risk (ARAD) enters into MCI, or that a subject having MCI enters into AD, for example by monitoring efficacy of a concomitant therapy.

For example, in one embodiment of the present disclosure, the person of skill can implement a process integrating mid-life central adiposity and AD family history positivity as prognostics for AD. Such a prognostic may provide unfortunate individuals manifesting these risks, the motivation and efficacy testing for risk intervention, i.e ., a form of preventive medicine.

Definitions

The present application makes reference to a number of microRNA. The reader will readily understand that the nucleotide sequence of these microRNAs is publically available and can be readily accessed through public databases of microRNAs or from scientific literature. For example, the reader can be referred to the miRBase Sequence Internet database which is currently managed by the Griffiths-Jones lab at the Faculty of Life Sciences, University of Manchester, the microRNA Internet database of the Sander lab from the Memorial Slaon-Kettering Cancer Center, and the like.

In one non-limiting embodiment, the herein described subject "at risk of Alzheimer's disease" or "ARAD" refers to a subject who is without cognitive deficits, and of younger age (40-69 years old), but may present at least one risk factor of developing AD. For example, subjects with a family history of AD ("FH-positive" or "FH+") are most readily recognized as being at higher risk for developing AD than those without a family history, with increased risk being calculated as 2-4 times higher in FH-positive individuals (Farrer et al., 1989; Vardarajan et al., 2014). Several other variables interact with the family history risk factor: for example, one study found that FH-positive women are at greater risk for AD development than FH-positive men; people with a sibling with AD are also more susceptible than those with a parent with AD (Devi et al., 2000).

In non-limiting embodiment, the herein described "mild cognitive impairment" or "MCI" (also known as incipient dementia, or isolated memory impairment) refers to a subject who has a brain-function syndrome involving the onset and evolution of cognitive impairments beyond those expected based on the age and education of the individual, but which are not significant enough to interfere with their daily activities. (See, e.g., Petersen et al. (1999), Arch. Neurol. 56 (3): 303-8.) It is often found to be a transitional stage between normal aging and dementia. Although MCI can present with a variety of symptoms, when memory loss is the predominant symptom it is termed "amnestic MCI" (now also called "late MCI") and is frequently seen as a prodromal stage of Alzheimer's disease. (See, e.g., Grundman et al. (2004) Arch. Neurol. 61 (1): 59-66.)

In one non-limiting embodiment, the herein described "substantially cell free biological sample" includes plasma or serum. The plasma or serum biological sample can be optionally first fractionated from whole blood prior to being frozen. This reduces the burden of extraneous intracellular RNA released from lysis of frozen and thawed cells, which might reduce the sensitivity of the amplification assay or interfere with the amplification assay through release of inhibitors to PCR such as porphyrins and hematin. "Fresh" plasma or serum may be fractionated from whole blood by centrifugation, using for instance gentle centrifugation at about 300-800 x g for about five to about ten minutes, or fractionated by other standard methods.

In one non-limiting embodiment, the herein described sample can be obtained by any known technique, for example by drawing, by non-invasive techniques, or from sample collections or banks, etc.

In one non-limiting embodiment, the present disclosure provides a kit which includes reagents that may be useful for implementing at least some of the herein described methods. The herein described kit may include at least one detecting agent which is "packaged". As used herein, the term "packaged" can refer to the use of a solid matrix or material such as glass, plastic, paper, fiber, foil and the like, capable of holding within fixed limits the at least one detection reagent. Thus, in one non-limiting embodiment, the kit may include the at least one detecting agent "packaged" in a glass vial used to contain microgram or milligram quantities of the at least one detecting agent. In another non-limiting embodiment, the kit may include the at least one detecting agent "packaged" in a microtiter plate well to which microgram quantities of the at least one detecting agent has been operatively affixed. In another non-limiting embodiment, the kit may include the at least one detecting agent coated on microparticles entrapped within a porous membrane or embedded in a test strip or dipstick, etc. In another non-limiting embodiment, the kit may include the at least one detecting agent directly coated onto a membrane, test strip or dipstick, etc. which contacts the sample fluid. Many other possibilities exist and will be readily recognized by those skilled in this art without departing from the invention.

In one non-limiting embodiment, the herein described RT-qPCR makes use of the methods and techniques described in U.S. Patent Application Publication No. 2013/0,040,303, incorporated by reference herein in its entirety.

As used herein, the expressions "circulating microRNA" generally refers to a microRNA found outside a cell and in a biological fluid sample, such as, saliva, urine, serum or plasma. Preferably, the biological sample is serum or plasma.

As used herein, the expression "biological sample" generally refers to a sample obtained from a biological subject, including samples of biological fluid origin, obtained, reached, or collected in vivo or in situ, which is not intracellular fluid obtained from lysis of tissue cells.

As used herein, the expression "obtaining a substantially cell free biological sample" refers to processing a biological sample or providing a biological sample which has been processed, for instance but without being limited thereto, by centrifugation, sedimentation, cell sorting, and the like, in order to substantially remove cells, such that when one aims to detect / measure the level of circulating microRNA, this detected level reflects extracellular circulating levels and minimizes detection of microRNA molecules which would be intra-cellular and/or released from cell lysis. Preferably, this expression refers to processing the biological sample. In the particular case of fluids such as plasma and serum, these are generally presumed to be cell-free; however in the practical sense, particularly under conditions of routine clinical fractionation, plasma and serum may occasionally be contaminated by cells. Nonetheless, plasma and serum are considered for the purposes of this invention as "substantially cell free" biological samples.

As used herein, microRNAs (miRNAs) are small (e.g., 18-25 nucleotides in length), noncoding RNAs that influence gene regulatory networks by post-transcriptional regulation of specific messenger RNA (mRNA) targets via specific base-pairing interactions. This ability of microRNAs to inhibit the production of their target proteins results in the regulation of many types of cellular activities, such as cell-fate determination, apoptosis, differentiation, and oncogenesis.

As used herein, a microRNA that is "differentially expressed" or "differentially present" is when the level thereof is "increased" or "decreased" relative to a reference level. In one non-limiting embodiment, the difference in level can be determined qualitatively, such as the visualization of the presence or absence of a signal. In another non-limiting embodiment, the difference in level can be determined quantitatively. In one non-limiting embodiment, the level may be compared to a diagnostic cut-off value, beyond which a skilled person is capable of determining the statistical significance of this level. In another non-limiting embodiment, the microRNA is differentially present if, for example, the mean or median level of the microRNA in a sample is calculated to be statistically significant from a reference level. Common tests for statistical significance include, among others, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann- Whitney and odds ratio, which are known to the person skilled in the art. In another non-limiting embodiment, the herein described "differentially present" represents a differential level of the biomarker of, e.g., at least 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.3, or more fold between the tested sample and a reference level.

In one non-limiting embodiment, the comparison of the herein described biomarker level relative to a reference level allows the person skilled in the art to select a candidate therapeutic compound at least partly based on the effect of the tested compound on the biomarker level. For example, the level of a microRNA in a sample can be "increased" when a host is contacted with the tested compound, for example, by an increase of about 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 90%, 100%, 200%, 300%, 500%, 1,000%, 5,000% or more relative to a reference level (e.g., in absence of the tested compound, or relative to a NEC, etc.)). Alternatively, the level of a microRNA in a sample can be "decreased" when the host is contacted with the tested compound, for example, by a decrease of about 99%, 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 1% or less relative to a reference level (e.g., in absence of the tested compound, or relative to a NEC, etc.) As used herein, a "low" level of a biomarker (microRNA) in a sample can be a level that is less than the level of the biomarker in a pool from a non-patient population. A "low" level of a biomarker in a sample can also refer to a level that is decreased in comparison to the level of the biomarker reached upon treatment, for example with an anti-AD compound. A "low" level of a biomarker can also refer to a level that is present in comparison to an individual that does not have AD (e.g., a NEC). .

As used herein, a "high" level of a biomarker (microRNA or target protein thereof) in a sample can be a level that is elevated in comparison to the level of a biomarker in a pool from a non-patient population. A "high" level of a biomarker in a sample can also refer to a level that is elevated in comparison to the level of the biomarker reached upon treatment, for example with an anti-AD compound. A "high" level of a biomarker can also refer to a level that is present in comparison to an individual that does not have AD (e.g., a NEC). As used herein, the terms "individual," "subject," and "patient," can be used interchangeably in the present specification, and generally refer to a human subject, unless indicated otherwise.

The terms "determining," "measuring," "evaluating," "assessing," and "assaying," can be used interchangeably in the present specification, and as used herein, generally refer to any form of measurement, and include determining if an element is present or not in a biological sample. These terms include both quantitative and/or qualitative determinations, which require sample processing and transformation steps of the biological sample. Assessing may be relative or absolute. The phrase "assessing the presence of can include determining the amount of something present, as well as determining whether it is present or absent. Preferably, the expression "measuring the expression level" refers to a quantitative determination.

As used herein, the term "about" for example with respect to a value relating to a particular parameter (e.g. concentration, such as "about 100 mM") relates to the variation, deviation or error (e.g. determined via statistical analysis) associated with a device or method used to measure the parameter. For example, in the case where the value of a parameter is based on a device or method which is capable of measuring the parameter with an error of ±10%, "about" would encompass the range from less than 10% of the value to more than 10% of the value.

As used herein, the term "MMSE" relates to the mini-mental state examination (MMSE) or Folstein test which is known to the person skilled in the art. This test is generally a brief 30-point questionnaire test that is used to screen for cognitive impairment. It is commonly used in medicine to screen for dementia. It is also used to estimate the severity of cognitive impairment at a given point in time, and to follow the course of cognitive changes in an individual over time, thus making it an effective way to document an individual's response to treatment.

In the time span of about 10 minutes it samples various functions including arithmetic, memory and orientation. It was introduced by Folstein et al. in 1975, [Journal of Psychiatric Research 12 (3): 189-98]. The standard MMSE form which is currently published by Psychological Assessment Resources is based on its original 1975 conceptualization, with minor subsequent modifications by the authors.

It must be noted that, as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents and plural referents include singular forms unless the context clearly dictates otherwise. Thus, for example, reference to "a subject polypeptide" includes a plurality of such polypeptides, reference to "the agent" includes reference to one or more agents and equivalents thereof known to those skilled in the art, reference to "nucleic acid molecules" includes reference to one or more nucleic acid molecules, and reference to "antibodies" includes reference to one or more antibodies and so forth.

With respect to ranges of values, the invention encompasses the upper and lower limits and each intervening value between the upper and lower limits of the range to at least a tenth of the upper and lower limit's unit, unless the context clearly indicates otherwise. Further, the invention encompasses any other stated intervening values.

A. Plasma / serum microRNA biomarkers in individuals with family history (FH) of

Alzheimer's disease (AD)

Many risk factors have been noted to be associated with sporadic AD, as opposed to familial AD which has clearly delineated genetic factors and develops earlier. The various risk factors for sporadic AD have been grouped into five risk profiles: genetic, metabolic, nutritional, cognitive, and psychological (Bilbul and Schipper, 2011; Schipper et al, 2011). Although genetic factors, including carrying one or more Apo ε4 alleles or having a family history of AD, cannot be modified, metabolic (including diabetes, obesity, hypertension, hyperlipidemia, and low exercise), nutritional, cognitive, and psychological risk factors can all be targeted for intervention to delay or halt disease progression regardless of genotype.

Among all the risk factors, individuals with a family history of AD ("FH-positive" or "FH+") are most readily recognized as being at higher risk for developing AD than those without a family history, as discussed previously.

Family history-specific biomarkers have already been examined in populations that are either cognitively normal or diagnosed as having amnestic mild cognitive impairment (MCI), an AD prodrome in which patients experience memor impairment while other cognitive functions and the ability to perform activities of daily living are not affected (Ringman et al, 2009).

Biomarkers that are abnormal in FH-positive family members compared to FH-negative individuals include differences in molecules within body fluids such as cerebrospinal fluid (CSF), changes in brain structure or metabolism revealed by imaging, and defects in cognition as measured by neuropsychological testing. Previous studies have reported increased levels of tau and Αβ42 in CSF (Honea et al., 2012; Lampert et al., 2013; Xiong et al., 2011) and serum (Abdullah et al, 2009). Additionally, increased superoxide dismutase activity in red blood cells of FH-positive subjects has been reported (Serra et al., 1994). Imaging studies have revealed various defects in β-amyloid burden, glucose metabolism, hippocampal volume, gray matter volume, and white matter microstructure (Adluru et al., 2014; Andrawis et al., 2012; Honea et al, 2011, 2012; Mosconi et al., 2007, 2014b). Furthermore, several groups have reported changes in cognition, such as learning and memory, hippocampal activation, visual- spatial and cognitive-motor processing, and brain connectivity, in non-AD subjects with a family history of AD (Chang et al, 2012; Hawkins and Sergio, 2014; Johnson et al, 2006; Okonkwo et al, 2014; Wang et al., 2012).

The present inventor surprisingly and unexpectedly discovered that a circulating microRNA known to be upregulated in dwarf mice and associated with longevity (Bates et al, 2010), a negative regulator of adipogenesis (Kang et al., 2013; Lin et al., 2009; Zhu et al, 2014), which is increased in adipose tissue of obese mice (Lin et al., 2009), and inhibits a variety of targets involved in atherosclerosis (Chen et al., 2012) has an expression level which was significantly decreased in substantially cell free sample of FH- positive individuals compared with their FH-negative counterparts. The reader is referred to Examples 1 and 2.

Such difference in expression level of circulating microRNA in substantially cell free sample correlated with FH of AD was surprisingly not seen for other circulating microRNAs miR-299, miR-21, let-7f and let-7d, which are markers that are known to be associated with AD and/or pathways believed to be involved in obesity or in AD genesis / progression.

Indeed, let-7d is dysregulated in AD serum (Kumar et al., 2013; Tan et al., 2014); let-7f is dysregulated in AD PBMC (Maes et al, 2009) and blood (Leidmger et al, 2013); miR-299 is predicted to target AD- associated genes such as presenilin-1, as well as obesity and adipogenesis-related genes such as adiponectin receptor 2; miR-21 is linked to BMI (Keller et al, 2011) and is a marker for cardiovascular disease (Han et al, 2015) is induced in mouse adipose tissue by a high fat diet (Kim et al, 2012), and inhibition of mir-21 reduces body weight, adipocyte size, and serum triglycerides in mice (Seeger et al, 2014).

Without being bound by any theory, the present inventor believes that decrease of expression levels of circulating miR-27a in substantially cell free sample of a subject may serve as a prognostic biomarker for assessing a likelihood risk of AD in yet asymptomatic subjects. In view of the data described in the present specification, the present inventor proposes a model which takes into account and links hereinbefore unlinked prior art data, as per the following:

It is known that miR-27a plays an important role in adipogenesis (Kang et al, 2013; Lin et al, 2009; Zhu et al, 2014). miR-27a is predicted to downregulate a variety of obesity and adipogenesis-related genes, including leptin and insulin-like growth factor 1 (Viesti A Collares et al, 2014) as well as ppary (PPAR) and adiponectin (Kim et al, 2010). Furthermore, miR-27a expression is reduced in adipocytes from obese mice (Kim et al, 2010). Interestingly, increased adiposity and metabolic syndrome, a collection of symptoms including hyperglycemia, hypertension, dyslipidemia, and abdominal obesity, are linked to the development of Alzheimer's. In mice, a high-fat diet can lead to AD pathology (Nuzzo et al., 2015). There is also an association between metabolic syndrome and memory, mood, cognition, and hippocampal volume in humans (Lamar et al., 2015). Additionally, one of the proteins targeted by miR-27a, leptin, is an adipocyte- derived hormone that regulates satiety and may play a role in AD (Ca et al, 2015). Increases in adiposity during mid-life, and the correlated changes in leptin and other hormones, may lead to dementia in later life (Ishii and Iadecola, 2015) (Kim et al., 2010). There is also a negative correlation between miR-27a levels and amyloid beta levels in CSF (Sala Fngerio et al., 2013).

Together with the present finding that miR-27a is significantly down-regulated in plasma from FH- positive individuals, these data suggest that miR-27a may be a key regulator of obesity, and a very early indicator of dementia onset resulting from the dysregulation of adipogenesis in high-risk of AD individuals.

B. Distinguishing mild cognitive impairment (MCI) from Alzheimer's disease (AD) by

increased expression of key circulating microRNAs

Within the AD cohort, a spectrum of disease states ranges from mild dementia to severe impairment. The Mini-Mental State Examination (MMSE) is a commonly used tool for estimating the severity of cognitive decline (Folstein et al., 1975; Galasko, 1998). Scores range from 0-30, with lower scores indicating higher levels of impairment. Probable AD patients can be categorized as having mild (MMSE scores of 21-24), moderate (MMSE scores of 10-20), or severe (MMSE scores of 0-9) dementia, while normal subjects (NEC: normal elderly control) are expected to score above 24 (Folstein et al, 1975; Galasko, 1998). Despite the MMSE's usefulness in estimating the severity of disease, some patients score outside of the commonly accepted cut-off values for normalcy, mild, moderate, and severe stages of disease. Additionally, non-invasive blood biomarkers are not routinely used in practice to determine disease stage in mild cognitively impaired (MCI) individuals and their probable AD counterparts.

There is therefore a need for a system and method to assist in distinguishing MCI individuals from normal elderly and probable AD individuals.

The present examples include a comparison of four cohorts: those with moderate or severe probable AD, mild probable AD, MCI, and NEC. In addition to examining the previously reported miR-34a and miR-34c (see, e.g., U.S. Patent Application Publication No. 2013/0,040,303 incorporated by reference herein in its entirety), in this study, the inventor also studied various other microRNAs, including miR-181b, miR-181c, let-7d, let-7f, let-7e, miR-200b, miR-141, miR-144, and miR-411. The results obtained in the present specification suggest that while miR-34c and miR-181c may be used as biomarkers to differentiate severe or moderate probable AD from MCI and/or NEC cohorts, they are only "fair" biomarkers to differentiate mild AD from MCI. On the other hand, based on the results described here, miR-411 emerges not only as a biomarker to identify moderate to severe AD patients from their mild cognitive impaired counterparts, but also as a probe to potentially distinguish MCI from the mild stage of Alzheimer's disease. In other words, the person of skill will be able to use the expression level of circulating miR-411 to identify those MCI subjects with high risk to advance into mild AD, since the expression level of miR-411 clearly increases between MCI reference subjects and mild AD reference subjects. The reader is referred to Examples 4 to 7.

The expression and functions of miR-411 have been investigated in several types of cancer, for example in human breast cancer (Guo et al. Molecular Medicine Reports 14, no. 4 (2016): 2975-2982). Guo et al report that the expression of miR-411 was significantly decreased in human breast cancer, and was associated with lymph node metastasis and histological grade. In addition, miR-411 has been shown to suppress cell proliferation, migration and invasion by directly targeting specificity protein 1 (SP1). Suggesting therapeutic implications that, for example, may be exploited for the treatment of cancer, in particular human breast cancer. The expression and functions of miR-411 have also been demonstrated that miR-411 as having potential as an indicator for acute graft-versus-host disease (aGVHD) monitoring (Zhang et al., Ann Hematol. 2016 Oct;95(l l): 1833-43).

To the inventor's best knowledge, the expression and functions of miR-411 have not been linked to AD pathogenesis and/or MCI.

In one embodiment, the present specification teaches a method that includes a combination of steps that transform a biological sample into disease markers and then further transform these markers into diagnosis markers. These markers provide information about the status of a sample using a combination of steps that, to the inventor's best knowledge, no one was performing or would have performed absent the teachings of the present specification. For example, while the present inventive concept is that circulating microRNA miR-411, is an indicator of the likelihood of AD vs. MCI or NEC, the inventive concept is, in one embodiment, ultimately embodied in a method or system that teaches how to apply the combined techniques of providing the sample, measuring the expression level of the circulating microRNA and detecting the presence of an abnormal expression.

Furthermore, the microRNA miR-411 has "natural" functions in the cells of the subject, such as suppressing cell proliferation, migration and invasion by directly targeting specificity protein 1 (i.e., SP1). However, while this microRNA has biological functions, this does not function as diagnostic marker when present in the cells. Only when practicing the herein described method or system, and by following the entire combination of steps does the circulating microRNA miR-411 become transformed into a product that provides an indication of AD vs. MCI or NEC likelihood status, i.e., a diagnostic marker.

EXAMPLES

EXAMPLE 1

Participant Demographics and Genetics

A total of 48 plasma samples, namely from a FH-positive cohort (n=24) and a FH-negative cohort (n=24) were included in this study. Samples were age-matched, with years of education, mini-mental status examination (MMSE) scores and ApoE genotype status also considered in the selection process as outlined in Table 1.

Table 1

The average age of the FH-positive cohort was 69.0, with an average formal education level of 14.0 years and MMSE score of 29.0; the FH-negative cohort had an average age of 69.2, average years of education of 15.0, and average MMSE score of 28.7. 11 individuals (46%) in the FH-positive cohort carry one ApoE4 allele, while only 25% of the FH-negative cohort subjects carry one or more ApoE4 alleles. The gender distribution for the two cohorts was also matched, with 71% and 67% females in the FH-positive and FH-negative cohorts, respectively.

EXAMPLE 2

Differential expression level of circulating microRNAs in plasma of FH-positive and FH-negative cohorts

In this example, the expression level of a number of circulating microRNAs is measured in substantially cell free samples from FH-positive and FH-negative cohorts in order to determine which microRNAs are "poor", "fair" or "good" biomarker to distinguish between FH-positive and FH-negative cohorts.

Fig. 1A shows the results for measurement of the expression level of circulating microRNA miR-299 in a plasma sample from FH-positive and FH-negative cohorts. The results show that the expression level of circulating microRNA miR-299 is significantly upregulated in plasma from FH-positive cohorts compared to plasma from FH-negative cohorts (p-value = 0.018). Based on this result, ROC analysis was performed to determine the ability of this microRNA to differentiate between FH-positive and FH- negative cohorts. Fig. IB shows that despite the fact that the expression level of circulating microRNA miR-299 is upregulated in plasma from FH-positive cohorts, there is an overlap between expression levels in the two groups, leading to an AUC value of 0.75. This AUC value of 0.75 indicates that measuring the expression level of circulating microRNA miR-299 in a substantially cell free sample of a subject is a "fair" test for distinguishing these two groups.

Fig. 2A shows the results for measurement of the expression level of circulating microRNA miR-21 in a plasma sample from FH-positive and FH-negative cohorts. The results show that the expression level of circulating microRNA miR-21 is significantly downregulated in plasma from FH-positive cohorts compared to plasma from FH-negative cohorts (p-value = 0.047). Based on this result, ROC analysis was performed to determine the ability of this microRNA to differentiate between FH-positive and FH- negative cohorts. Fig. 2B shows that despite the fact that the expression level of circulating microRNA miR-21 is downregulated in plasma from FH-positive cohorts compared to plasma from FH-negative cohorts, there is an overlap between expression levels in the two groups, leading to an AUC value of 0.65. This AUC value of 0.65 indicates that measuring the expression level of circulating microRNA miR-21 in a substantially cell free sample of a subject is a "poor" test for distinguishing these two groups.

Fig. 3A shows the results for measurement of the expression level of circulating microRNA let-7d in a plasma sample from FH-positive and FH-negative cohorts. The results show that the expression level of circulating microRNA let-7d is significantly downregulated in plasma from FH-positive cohorts compared to plasma from FH-negative cohorts (p-value = 0.013). Fig. 3B shows the result of the ROC analysis, which revealed an AUC value of 0.64, indicating that measuring the expression level of circulating microRNA let-7d in a substantially cell free sample of a subject is a "poor" test for distinguishing these two groups.

Fig. 4A shows the results for measurement of the expression level of circulating microRNA miR-27a in a plasma sample from FH-positive and FH-negative cohorts. The results show that the expression level of circulating microRNA miR-27a is also downregulated in plasma from the FH-positive cohorts compared to plasma from FH-negative cohorts (p-value of 0.002). Fig. 4B shows the results of the ROC analysis, which revealed an AUC value of 0.83, indicating that measuring the expression level of circulating microRNA miR-27a in a substantially cell free sample of a subject is a "good" biomarker for distinguishing between the two groups. The AUC, sensitivity and specificity values for each of the above measured circulating microRNA are summarized in Table 2.

Table 2

Finally, no significant difference was found between the FH-positive and FH-negative cohorts for the expression level of circulating microRNAs miR-133a, miR-27b, and let-7f in plasma samples (Figs 5A, 5B and 5C, respectively).

Because leptin is a predicted target of microRNA miR-27a according to targetscan.org (Agarwal et al.) and is associated with obesity, the inventor predicted based on the results above that individuals in the FH-positive cohort would have higher levels of plasma leptin than those in the higher miR-27a- expressing FH-negative group. Indeed, the inventor found a trend of increased leptin in the FH-positive cohort, although there was not a statistically significant relationship with the small number of samples used in that assay (data not shown).

Fig. 10A illustrates a graph showing the average Ct determined from control spiked-in Cel-54. Fig. 10B illustrates a graph showing the relative expression levels of circulating microRNA miR-34a in plasma sample from FH+ cohort (n=7) and FH- cohort (n=7) identified in Fig. 9A, 9B and 9C, where FH+ represents FH-positive and obese subjects, whereas FH- represents FH-negative and normal BMI & waist circumference subjects. Fig. IOC illustrates a graph showing the relative expression levels of circulating microRNA miR-34c in plasma sample from FH+ cohort (n=6) and FH- cohort (n=8) identified in Fig. 9A, 9B and 9C, where FH+ represents FH-positive and obese subjects, whereas FH- represents FH-negative and normal BMI & waist circumference subjects. The results show that the expression level of circulating microRNA miR-34a is statistically significantly upregulated in plasma samples of FH-positive and obese individuals relative to FH-negative and normal (non-obese) individuals (p value = 0.018) and that the expression level of circulating microRNA miR-34c is not statistically different between plasma samples from the two cohorts.

In sum, these results show that the expression level of circulating miR-34a is increased in substantially cell free samples (plasma) of multiple AD-relative FH+/obese subjects. Moreover, the rise of the expression level of miR-34a corresponds to the increase of waist circumference (cm) and BMI, (Fig. 11A). These results suggest that there is a correlation in that higher BMI values (Fig. 11A) or bigger waist circumference measured by centimeter (cm) (Fig. 11B), correlates with higher expression level of circulating miR-34a in the plasma samples. The extreme FH+/abdominal obese individuals (squares) are in general segregated from FH-/normal BMI, or normal waist size (round dots); the significance is represented by p-values. Statistical analysis of the linear relationship between expression level of circulating miR-34a in plasma sample and BMI or waist sizes is represented by V correlation coefficient values. These values are 0.73 between expression level of circulating miR-34a & BMI. and 0.6 between expression level of circulating miR-34a & waist circumference (cm), suggesting a trend of positive correlation between this microRNA's expression level in plasma and BMI or waist size increases.

With respect to expression levels of circulating miR-34c in substantially cell free sample, if one subdivides the FH+ and FH- to:

Group 1 : FH+/high BMI/WC,

Group 2: FH+/normal BMI/WC;

Group 3: FH-/high BMI&WC; and

Group 4: FH-/normal BMI/WC,

Then the expression level of circulating miR-34c is a good biomarker for comparison of Group 1 vs. 4 (Figure 15E) as well as Group 1 vs. Group 2 (Figure 15A).

Materials and methods for Examples 1 and 2

Participants selected for blood biomarker study met the following inclusion criteria: a. Persons with (high risk) or without (low risk) family history of late-onset AD in parents or

siblings; b. Stable clinical condition, without significant abnormalities of self-reported cancer, renal disease, uncontrolled diabetes, heart disease, etc. Potential participants were excluded from the study based on the following criteria: a. Known autosomal dominant mutation of APP or presenilin (Familial AD); b. Personal history of major neurological condition, including stroke, bram tumor, epilepsy,

cerebrovascular disease, or degenerative CNS disorder; c. Recent history (one year) of tobacco and/or substance abuse; d. Personal history of chronic psychiatric disorders, e.g. major depression, schizophrenia, bipolar disorder, autism, etc.

Blood samples (-30 mL) were drawn from the volunteers' antecubital veins with either an 18-24 gauge butterfly needle or regular 22-gauge needle, and stored in EDTA Vacutainers^® Using Ficoll-Paque Plus (GE Healthcare, Piscataway, NJ), plasma was isolated. Plasma samples were aliquotted and stored at - 80°C; plasma samples with hemolytic red blood cell (RBC) contamination as indicated by an absorbance at 414 nm of higher than 0.2 , or showing Bioanalyzer profiles with more than one single peak at 40 nt, were excluded as per our previous study (Bhatnagar et al., 2014). Aliquots of plasma were thawed and spun and the pellet discarded. Qiagen's miRNeasy serum/plasma kits were used to isolate RNA from plasma. Synthetic Cel-miR-54 and Cel-miR-39 spike-in (Qiagen) was added to each reaction at a concentration of 33 fmol, directly before adding chloroform to the samples. The concentration of the isolated RNA was measured with an Epoch spectrophotometer (Biotek, Winooski, Vermont). Isolated RNA templates were used to make cDNA specific for each targetor spike-in miRNA, by applying 50 ng of RNA to a Taqman microRNA Reverse Transcription kit (Life Technologies, Carlsbad, California), and reacting with the miRNA-specific 5X primer provided in the Taqman small RNA assays (Life Technologies). Complementary DNA was synthesized in a Veriti^® 96-well Thermalcycler (Life Technologies). The cDNA template was used to perform qPCR, along with 20X probes provided in the Taqman small RNA assays (Life Technologies,) and Bullseye qPCR Master Mix (MidSci, St. Louis, Missouri). Reactions were performed in triplicate using an ABI7500 Fast Real-time PCR System (Life Technologies). The efficiency of RNA extraction and cDNA synthesis was monitored by measuring Ct values for Cel-miR-39 and Cel-miR-54, using the Taqman small RNA assays for these synthetic microRNAs. Human plasma samples were thawed, diluted 100-fold, and applied to the Quantikine^® Human Leptin ELISA (R&D, Minneapolis. Minnesota).

Statistical analyses were conducted using MS Excel and SPSS 21.0 statistical software (IBM). For multiple group comparisons, a one-way ANOVA followed by Fisher's Least Significant Difference (LSD) test was implemented (Hayter, 1986). Statistical significance was defined as p < 0.05. Samples with excessively high or low Ct values for Cel-miR-54 or Cel-miR-39 were excluded from further analysis. For data analysis of microRNA levels in circulating plasma, the AAC_t method was employed, using the average of the values for Cel-miR-54 and Cel-miR-39 as the reference. To determine how well candidate microRNAs distinguish between FH+ and FH- groups, Receiver Operating Characteristic (ROC) analyses were utilized. These analyses yield several values: 1) specificity, which discerns different stages or states of a state or condition; 2) sensitivity, which identifies individuals with a certain state or condition in a given population; 3) area under the curve, which indicates the accuracy of the test. These values are on a scale of 0.5-1.0, with 1.0 representing a perfect test. AUC values between 0.6 and 0.69 are considered indicative of a poor test; between 0.7 and 0.79 a fair test; between 0.8 and 0.89 a good test; and between 0.9 and 1 an excellent test (El Khouli et al., 2009). Calculations for these tests were carried out as previously described (Bhatnagar et al., 2014; Lalkhen and McCluskey, 2008; Zweig and Campbell, 1993).

EXAMPLE 3

Selection of plasma samples for quantitative PCR assays and identification of expressed microRNA targets

Plasma samples used for this study were selected from four cohorts, which include individuals with probable moderate or severe sporadic AD, probable mild sporadic AD, MCI, or NEC individuals. Average ages of the cohorts were 75.3 for NEC, 78.9 for MCI, 78.2 for mild AD, and 77.7 for moderate- severe AD samples. In addition, probable AD patients with MMSE scores of 25 or higher were excluded from our study, as were NEC volunteers scoring below this cut-off. Moderate and severe AD patients were identified as having MMSE scores of 18 or less. Table 4 shows that the overall average MMSE was 13.3 for moderate and severe AD patients, 23.1 for mild AD patients, and 28.7 for NEC with the MCI group's cognitive ability determined by the Montreal Cognitive Assessment (MoCA) scoring matrix. The average MoCA score for the MCI group was 20.7. The MMSE scores of the NEC, MCI, and mild AD and moderate-severe AD cohorts are compared with education level, age, and the distribution of ApoE genotypes.

Table 3

Following the initial selection of samples by their cognitive assessment scores for each group, additional acceptance criteria involving RNA sample quality were also considered (Schroeder et al., 2006). Samples with high levels of hemolysis as measured by absorbance at 414 nm were excluded. Samples with no contaminating cellular ribosomal RNA as measured by the Agilent Bioanalyzer, were considered non- contaminated and selected for use in the study. Additionally, we employed the use of spiked-in synthetic C. elegans microRNAs to monitor the consistency of RNA extracted and the quality of cDNA synthesized. The consistent measured levels of cel-miR-39 and cel-miR-54 between samples and across cohorts suggests that these assays are stringent, and the cDNA synthesized is of optimal quality with minimal putative RNA inhibitors present.

EXAMPLE 4

MicroRNA miR-34c as a biomarker for distinguishing probable moderate-severe AD from MCI and for distinguishing mild AD from MCI

Circulating microRNA miR-34c has been shown as being upregulated in plasma from probable AD patients compared to plasma from NEC volunteers (Bhatnagar et al, 2014). Furthermore, miR-34c is equally abundant in plasma from mild and moderate AD patients. In the present example, the inventor examined the expression level of circulating microRNA miR-34c in substantially cell free samples from various cohorts to test its ability to act as an AD/MCI biomarker.

First, the expression level of circulating microRNA miR-34c was measured in plasma from moderate- severe AD, mild AD, MCI, and NEC cohorts. The results illustrated in Fig. 6A show that the expression level of circulating microRNA miR-34c is more highly expressed in plasma from moderate -severe and mild AD cohorts, compared to both MCI and NEC cohorts. However, there was no significant difference between the moderate-severe AD and mild AD cohorts (LSD p-value=0.392; Bonferroni p-value=0.99), or between the MCI and NEC groups (LSD p-value=0.711; Bonferroni p-value=0.999) (Fig. 6A).

Second, ROC analysis was performed to determine the efficacy of the expression level of circulating miR-34c in plasma samples as a biomarker to distinguish the four cohorts from one another (Figs. 6B- 6E). The ROC data indicates that miR-34c performs as a "fair" test to differentiate moderate-severe AD from MCI (AUC=0.78); as a "good" test to distinguish moderate-severe AD from NEC (AUC=0.82); as a "fair" test to differentiate mild AD from NEC (AUC=0.71); and as a "poor" test to distinguish mild AD from MCI (AUC=0.68).

EXAMPLE 5

MicroRNA miR-181c as biomarker for probable moderate-severe and mild AD

Members of the miR-181 family have been implicated in neuro-inflammation (Hutchison et al, 2013) and Alzheimer's disease (Maes et al., 2009; Schonrock et al, 2012). In the present example, the inventor examined the expression level of circulating microRNA miR-181c in substantially cell free samples from various cohorts to test its ability to act as an AD/MCI biomarker.

First, the expression level of circulating microRNA miR-181c was measured in plasma from moderate- severe AD, mild AD, MCI, and NEC cohorts. The results illustrated in Fig. 7A show that the expression level of circulating microRNA miR-181c is more highly abundant in plasma samples from moderate- severe AD patients than from MCI patients (LSD p-value < 0.001: Bonferroni p-value = 0.004) and NEC volunteers (LSD p-value = 0.004; Bonferroni p-value=0.022) (Figure 23A). Additionally, the expression level of circulating microRNA miR-181c is more highly expressed in the mild AD cohort than in the MCI cohort (LSD p-value < 0.001; Bonferroni p-value = 0.004) and NEC cohort (LSD p-value = 0.005; Bonferroni p-value = 0.027). However, there are similar expression levels of circulating miR-181c in plasma from moderate -severe and mild AD cohorts (LSD p-value = 0.898; Bonferroni p-value = 0.999). There are also similar expression levels in the MCI and NEC cohorts (LSD p-value = 0.493; Bonferroni p-value = 0.999).

Second, ROC analysis was performed to determine the efficacy of circulating miR-181c in plasma samples as a biomarker to distinguish the four cohorts from one another. ROC data shows that miR-181c is able to effectively differentiate moderate-severe AD samples from both MCI and NEC cohorts, with AUC values of 0.86 and 0.79, respectively (Figs 7B-7C). This indicates that miR-181c serves as a "good" biomarker for separation of moderate-severe AD and MCI, and a "fair" biomarker for separation of moderate-severe AD and NEC. However, miR-181c is a "poor" biomarker for distinguishing between mild AD and either MCI (AUC=0.63) or NEC (AUC=0.60) (Figures 23D-23E). EXAMPLE 6

MicroRNA miR-411 as biomarker to distinguish MCI and NEC from mild, moderate and severe

AD

In the present example, the inventor examined the expression level of circulating microRNA miR-411 in substantially cell free samples from various cohorts to test its ability to act as an AD/MCI biomarker.

Although neither circulating miR-34c nor miR-181c perform as "good" biomarkers to distinguish mild AD from MCI and NEC cohorts, qPCR results for measuring the expression level of circulating miR-411 in plasma indicate that this microRNA may be a promising biomarker to distinguish between all stages of probable AD and both MCI and NEC cohorts.

The results obtained show that there are significant differences when comparing moderate -severe AD to either MCI or NEC (LSD p-values < 0.001; Bonferroni p-values < 0.001), and when comparing mild AD to either MCI or NEC (LSD p-values < 0.001; Bonferroni p-values < 0.001) (Figure 24A). However, no difference is detected between either the moderate-severe and mild AD groups (LSD p-value = 0.136; Bonferroni p-value = 0.818), or between the MCI and NEC cohorts (LSD p-value = 0.551; Bonferroni p- value = 0.999) (Fig. 8A).

ROC analysis was performed to determine the effectiveness of miR-411 in separating the four cohorts examined. AUC values were calculated of 0.92 when comparing moderate-severe and MCI groups, and 0.99 when comparing moderate-severe and NEC groups (Figs 8B-8C). For the mild AD comparisons, the AUC values are of 0.93 when compared to the MCI cohort, and 0.98 when compared to the NEC cohort (Figs 24D-24E). These AUC values indicate that miR-411 is an "excellent" biomarker to separate all stages of AD from both MCI and NEC.

EXAMPLE 7

Correlation between microRNA biomarker expression level and MMSE scores

In the present example, the inventor used a correlation analysis to examine the relationship between MMSE scores and the expression level of circulating microRNA in substantially cell free samples.

The relative expression of each of the circulating microRNAs was plotted against the MMSE score; p (rho) and p-values were calculated for miR-34c, miR-181c, and miR-411.

Statistically significant inverse relationships were found for all three circulating microRNAs: p=0.001 for miR-34c; p = 0.011 for miR-181c; and p < 0.001 for miR-411. However, for miR-34c and miR-181c the^-values of -0.37 for miR-34c and -0.3 for miR-181c are relatively low, indicating that high expression levels of these microRNAs is not a strong predictor of a low MMSE score or more severe cognitive decline. On the other hand, the -value for miR-411 is -0.75, which shows a statistically strong negative correlation between expression levels of this microRNA and MMSE score, i.e. higher miR-411 levels in the plasma are correlated with lower MMSE scores.

Materials and methods for Examples 3 to 7

All subjects in the cohort classified as MCI met the clinical criteria for MCI as defined by Winblad's group (Winblad et al, 2004). All had a history of memory decline in the last 1-4 years. All MCI subjects were found to have objective memory impairment on the JGH Memory Clinic Assessment (unpublished), which contains elements of the CERAD, CDR, and Toronto Behavioural Neurology Assessment batteries appropriate for mild dementia subjects (Darvesh et al., 2005). The Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005) exam was also administered to these individuals, as the MMSE score lacks sensitivity for detecting cognitive impairment at the MCI stage; all MCI individuals scored below 26 out of 30 on the MoCA assessment. The MCI subjects did not meet NINCDS-ADRDA criteria for the diagnosis of probable AD, or the DSM-III criteria for dementia (McKhann et al., 1984). Subjects diagnosed as probable AD met clinical criteria for dementia and probable AD (McKhann et al., 1984). The Mini-Mental State Examination (MMSE) (Folstein et al, 1975) was administered to all subjects, and scores were used as inclusion criteria for the study.

Ficoll-Paque Plus (GE Healthcare, Piscataway, NJ) was used to isolate the plasma fraction from 30 mL blood samples collected in EDTA Vacutainers⁰. RNA was extracted from plasma samples using the miRNeasy Serum Plasma Kit (Qiagen, Venlo, Limberg, Netherlands) following the manufacturer's instructions, with the addition of a second extraction step following the initial chloroform extraction, adapted from a previous study (Burgos et al, 2014). For quality control of extraction efficiency and cDNA synthesis in qPCR assays, 33 frnol of both cel-miR-39 and cel-miR-54 (Qiagen) were added to the samples before adding chloroform; the RNA concentrations of the samples were quantified using the Epoch Spectrophotometer (Biotek, Winooski, VT), and cellular contamination was assessed on an Agilent 2100 bioanalyzer (Agilent Technologies, Waldbronn, Germany).

Samples were selected based on a number of factors including age, MMSE and MoCA scores, and the absence of hemolysis in the samples. MMSE scores for samples from AD cohorts were required to be below 25; for NEC cohorts, MMSE scores were equal to or higher than 27. Samples from MCI cohorts scoring in the 17-23 range on the MoCA were included. Additionally, samples from probable AD cohorts were classified as mild, moderate, and severe based on the following MMSE scores: 0-9 indicated severe AD, 10-20 moderate AD, and 21-24 mild AD (Folstein et al, 1975; Galasko, 1998). Plasma samples with an absorbance reading above 0.25 at 414 nm were excluded to avoid interference from hemolysis (Kirschner et al, 2011). RNA isolated from the aqueous phase of plasma was used to generate cDNA by means of the Taqman" MicroR A Reverse Transcription Kit (Applied Biosystems, Carlsbad, CA). Purified RNA was used to synthesize first strand cDNA, using specific miRNA stem-loop primers (Life Technologies, Thermo Fisher, Grand Island, New York) for each microRNA target (catalog number 4427975 and assay IDs: miR-34c: 000428; miR-181c: 000482; miR-411: 001610; cel-miR-39: 000200; cel-miR-54: 001361) to determine these microRNAs' expression levels by real time quantitative PCR. The reactions were carried out on an ABI 7500 or ABI 7500 Fast real-time PCR system (Applied Biosystems) with Bullseye TaqProbe qPCR 2x Mastermix (ABI, Foster City, California); all reactions were performed in triplicate to reduce variation.

All statistical analyses were conducted using MS Excel 2010 and SPSS 21.0 statistical software (IBM). For multiple group comparisons, a one-way ANOVA followed by Fisher's Least Significant Difference (LSD) test and the Bonferroni post hoc test, to control for familywise type 1 error, were implemented (Hayter, 1986; Zweig and Campbell, 1993). Statistical significance was defined as p < 0.05 for all analyses. For determining relative expression, the AACt method was utilized, where ACt = normalized Ct value of target microRNA - Ct value of spike-in reference microRNA as described in U.S. 2013/0,040,303, which is hereby incorporated by reference for all purposes. Any sample for which the extraction or cDNA step was inefficient was excluded, as indicated by high Ct values for cel-miR-39 or cel-miR-54.

Receiver Operating Characteristic (ROC) analyses were performed with the results of the qPCR data after the above calculations. In general, these analyses involve three parameters: sensitivity, specificity, and accuracy. All three are denoted by numerical indices less than 1; the closer to 1, the better the score for biomarkers to differentiate between two states. The numerical values for sensitivity represent the ability of a test to identify individuals within a given cohort exhibiting a specific state. The numerical values for specificity represent the power to distinguish a specific state from others.

ROC analysis also provides numerical values for the area under the curve (AUC), on a scale from 0.5-1.0: the higher the value, the more accurate the test. AUC values between 0.9 and 1.0 were considered to be "excellent"; values between 0.8 and 0.89 were "good"; values between 0.7 and 0.79 were "fair"; values between 0.6 and 0.69 were "poor"; values below 0.6 indicated "no usefulness" as a biomarker (El Khouli et al., 2009). Calculation of sensitivity, specificity and accuracy followed published methods (Lalkhen and McCluskey, 2008; Zweig and Campbell, 1993) using the same parameters and formulas as previously described (Bhatnagar et al., 2014). Spearman correlation (p) was used to measure the strength of linear association between MMSE scores and relative expression levels of miRNAs studied. The p (rho) value ranges from 0 to 1, with a positive or negative relationship. The closer the value of rho to 1, the stronger the association between the two variables (Taylor, 1990).

EXAMPLE 8

In the following example, the present inventor tested whether the expression level of a circulating microRNA in a substantially cell free sample could be correlated with an abdominal obesity marker.

Abdominal obesity is linked to many old age diseases, and is a major risk for Metabolic Syndrome, involving diabetes, stroke and heart disease. Strong links of AD to Metabolic Syndrome suggest that AD may be thought of as 'Type 3 diabetes' [37-39], Countless clinical, diagnostic and therapeutic trials have been pursued to optimize healthcare for Metabolic Syndrome, to control diabetes and reduce strokes and heart attacks. In addition, numerous pharmaceutical, nutraceutical, and weight control programs, from drastic surgical laparotomy to exercise, attempt to control obesity. The efficacy of all these aggressive countermeasures to control weight gam is largely based on rudimentary physical observations such as weight, BMI and waist circumference measurement, along with blood pressure, cholesterol levels, etc. To date, no efficacy test at the genomic level has surfaced, to evaluate the risk of dementia beyond the phase of Metabolic Syndrome control. Moreover, the risk of mid-life obesity and FH+ leading to AD dementia is not the sum total of Metabolic Syndrome [40]; genes involved in forming cortical plaques and tangles may include, but are not limited to, signaling for diabetes, heart disease and stroke.

Figs. 9A and 9B illustrate tables for individuals tested for the biomarkers in Figs. 10A, 10B and IOC. Participants selected met the following criteria:

Inclusion criteria: a. Persons with positive family history in parents or siblings (high risk) [FH- positive] or without (low risk) [FH-negative] of late-onset AD; b. age 40-69 inclusive, of either gender; and c. absence of symptomatic cognitive decline, by neurological assessment.

Exclusion criteria: a. autosomal dominant mutation of APP or Presenilin (Familial AD); b. personal history of major neurological disease, i.e. stroke, brain tumor, epilepsy, or cerebrovascular disease, etc. ; c. Recent history (one year) of tobacco and/or substance abuse; d. uncontrolled diabetes, and e. Personal history of chronic psychiatric disorders, e.g. major depression, schizophrenia, bipolar disorder, autism, etc.

Samples from 40-69 year old donors were collected from the Alzheimer Risk Assessment Clinic (ARAC), a tertiary care facility for dementia risk ascertainment and mitigation, established at the McGill University Jewish General Hospital in Montreal. The biobank size of Family history positive (FH-positive or FH+) and controls, Family history negative (FH-negative or FH-), are listed in the following Table 4: Table 4

Cohorts Female

j&fi three phases) 295

NEC controls Illlil

f&M Cognitive Impaired 275^™"

| Ci~>AD converter otal 945 1 56%

Categories were separated by High BMI > 25, including overweight (25-29.9), obese (> 30), and High Waist, including men: > 94 cm (overweight) and > 102 cm (obese), as well as women: > 80cm (overweight), > 88cm (obese). The high-waist sub-cohorts were further separated by age groups, as listed in Fig. 9B.

The following two sub-cohorts were used: extreme FH+/High BMI & high waist circumference (WC) cohort and controls, FH-/normal BMI & waist.

The selection criteria "extreme FH+" include individuals with BMI > 28 and WC of 98-129 cm for women, and 117-125 for men, plus > 2 first-degree relatives (parent or siblings) or direct lineage (parent & grandparents (Gr.) & great-grandparents (Great Gr.). The controls are those with no AD relatives, BMI < 24 and WC of 92 cm or less for men, and 69-79 for women. [In general, overweight BMI = 25-29.9 (overweight); > 30 (obese); WC > 94 cm (overweight) and WC > 102 cm (obese) for men; and for women > 80 cm (overweight) and > 88 cm (obese)]. The two cohorts are age-matched, with education > 10 years; ApoE 4 allele status was recorded and linked.

The qPCR assays and protein-based assays followed the roadmap to link biochemical and molecular data with clinical information, following the flow path illustrated in Fig. 9C, with data from comprehensive clinical/cognitive assessment, registered indices of BMI and waist circumference, self-disease status, AD family history, etc. All subjects, including FH+, were evaluated for asymptomatic status by a battery of neuropsychological tests for symptomatic manifestation of cognitive decline, by the mini-mental status examination (MMSE), the Montreal Cognitive Assessment (MoCA) [41], and/or the Logical Memory Test of the WAIS-R [42], for the absence of both AD dementia and mild cognitive impairment (MCI), along with the JGH MCI Assessment (unpublished), which contains elements of the CERAD, CDR, and Toronto Behavioral Neurology Assessment batteries appropriate for mild dementia subjects [43-46], This patient information was linked to wet-lab data by a battery of statistical analyses such as the non- parametric W^Tilcoxon and Mann-Whitney tests [47, 48], to evaluate statistical significance of differences between extreme FH+/obese and control cohorts, as well as calculation of specificity, accuracy, and sensitivity by receiver operating characteristic (ROC) curves, or the area under the curve (AUC) method [49- 51], following our publications [34, 35].

Plasma miR-34a as a biomarker for extreme FH+/abdominal obesity

Figs. 10B and IOC show the results of comparing the expression level of herein described biomarkers from plasma samples of FH+ individuals (i.e., with more than one AD relative) and which have high BMI & high waist circumference, with FH-mmus individuals (i.e., FH-negative) and which have normal BMI & waist circumference. These results show that the expression level of circulating miR-34a is statistically increased in the FH+/obese individuals' plasma (p = 0.018), while the expression level of circulating miR-34c does not statistically discern between the two cohorts (p = 0.268). Moreover, the rise of the expression level of circulating miR-34a corresponds to the increase of waist circumference (cm) (p = 0.009) and BMI (p = 0.003).

These results suggest that the higher BMI values (Fig. 11A) or bigger waist circumference measured by centimeter (cm) (Fig. 1 IB), correlates with miR-34a abundance in plasma samples. The extreme FH+/abdominal obese individuals (squares) are in general segregated from FH-/normal BMI, or normal waist size (round dots); the significance is represented by p-values. Statistical analysis of the linear relationship between plasma expression levels of circulating miR-34a and BMI or waist sizes is represented by V correlation coefficient values. These values are 0.73 between miR-34a & BMI, and 0.6 between miR-34a & waist circumference (cm), suggesting a trend of positive correlation between the expression level of circulating microRNA miR-34a in plasma and BMI or waist size increases.

Distinguishing FH+/obese cohorts from counterpart cohorts

In this example, the inventor tested whether the expression level of circulating microRNA miR-34a in a substantially cell free sample can be correlated with classic biomarkers of obesity.

Biochemical testing of leptin and glucose metabolism was established for high-throughput protein-based assays, for pairwise analysis of relationship between miR-qPCR values and leptin- or glucose values in plasma, and/or between miR-qPCR values and BMI or waist circumference (WC) values, for sensitivity, specificity, and accuracy, to distinguish FH+/obese cohorts from counterpart cohorts.

Biochemical assays (ELISA) were performed with plasma samples of the two cohorts listed in Fig. 9B, yielding indices of plasma leptin and glucose levels. The results with these two cohorts, i.e. Extreme FH+/obesity and controls, are analyzed in the following Table 5: Table 5

Pair comparison between plasma circulating miR-34a expression level vs. leptin, vs. glucose, or vs. BMI and waist size (cm), appear in Fig. 12A and Fig. 12B.

Leptin and glucose levels correspond to increases of BMI and waist circumference, and the FH+ cohort segregates from FH-negative controls (Figs. 13A and 13B). The ROC curves were determined and demonstrate an AUC for miR-34a of 0.94 and for Leptin of 1.00 (Fig. 14A) and an AUC for glucose of 0.96.

These results are to the inventor's knowledge, the first to show that: a. in middle age, the expression level of circulating microRNA miR-34a in plasma, which has been demonstrated as an AD biomarker, is already increased in circulating plasma in FH+/obese individuals; and

b. increased BMI, waist circumference, leptin, and glucose levels correspond with each other [Figs.

13 A, 13B. 13C, and 13D] as well as to the expression level of circulating microRNA miR-34a increase in plasma samples, and can be used to distinguish the two cohorts, i.e. extreme FH+/abdominal obese individuals and control counterparts, with high levels of sensitivity, specificity and confidence (Figs. 14A) and 14B)).

Thus, leptin, glucose and miR-34a levels in plasma are "excellent" blood-based biomarkers with high confidence levels, sensitivity, specificity, and area under the curve (AUC), to distinguish extreme FH+/abdominal obese individuals in middle age from counterparts, i.e. FH-/normal BMI and waist circumference.

Plasma miR-34c as a biomarker for extreme FH+/abdominal obesity

The results obtained in the present experiment show that the expression level of circulating miR-34c in a substantially cell free sample was significantly upregulated in the FH+ High BMI/WC group when compared to both the FH+ Normal BMI/WC group, p = 0.003 (Fig. 15A), as well as the FH- Normal BMI/WC group, p = 0.012 (Fig. 15E). The levels of miR-34c showed increased expression in the FH- High BMI/WC group compared to the FH+ Normal BMI/WC group, p = 0.024 (Fig. 15F). ROC analysis was performed to determine the ability of miR-34c to differentiate between FH+ High BMIAVC and FH+ Normal BMI/WC groups. The expression level of circulating microRNA miR-34c in a substantially cell free sample was found to be a "good" biomarker to distinguish between the group with a High BMIAVC as compared to a group with a Normal BMIAVC when both groups had a family history of Alzheimer's disease, AUC = 0.88, p = 0.003 (Fig. 16A). ROC analysis was conducted between FH+ High BMIAVC and FH- Normal BMIAVC. The AUC value was 0.77, determining miR-34c to be a "fair" test in differentiating High BMIAVC individuals from Normal BMIAVC individuals when the High BMIAVC cohort possessed a family history of AD (Fig. 16B). Sensitivity and specificity values for ROC analyses can be found on Table 6.

Table 6

Leptin was up regulated in the FH+ High BMIAVC group when compared to Normal BMIAVC groups with and without a family history of AD (Fig. 17A and 17E). The concentration of leptin was greater in FH+ High BMIAVC when compared to both FH+ Normal BMIAVC, p = 0.004 (Fig. 17A) and FH- Normal BMIAVC, p = 0.006 (Fig. 17B). There were increased levels of glucose in the FH+ High BMIAVC group compared to the FH- Normal BMIAVC group (Fig. 18E).

The person of skill will realize that not all the FH+ are with high BMI & WC, thus they may be at reduced risk, albeit, still risk plus. Corollary to this last statement, not all high BMI & WC are FH+, thus, those without FH history may be at lower risk. Nevertheless, the FH+ high BMI & WC linked with high leptin (Figure 17E), glucose and miR-34c links the results obtained for FH+/High BMI & WC with the results obtained for Ale (Figure 18E and Figure 20 A-C).

Potential Targets for circulating microRNA 411

In an effort to better understand the herein described data, the inventor performed an analysis to identify the genes which are potential targets for circulating microRNA miR-411.

In the following Table 7, the inventor performed an analysis to identify the human genes associated with obesity, the mRNA of which would be targets for circulating microRNA 411: Table 7

In the following Table 8, the inventor performed an analysis to identify the human genes associated with the Alzheimer's disease pathway, the mRNA of which would be targets for circulating microRNA 411:

Table 8

In the following Table 9, the inventor performed an analysis to identify the human genes associated with the mTor signaling pathway, the mRNA of which would be targets for circulating microRNA 411 : Table 9

In the following Table 10, the inventor performed an analysis to identify the human genes associated with the fat digestion and absorption pathway, the mRNA of which would be targets for circulating microRNA 411:

Table 10

In the following Table 11, the inventor performed an analysis to identify the human genes associated with the insulin resistance pathway, the mRNA of which would be targets for circulating microRNA 411 :

Table 11

In the following Table 12, the inventor performed an analysis to identify the human genes associated with the insulin secretion pathway, the mRNA of which would be targets for circulating microRNA 411:

Table 12

In the following Table 13, the inventor performed an analysis to identify the human genes associated with the insulin signaling pathway, the mRNA of which would be targets for circulating microRNA 411:

Table 13

In the following Table 14, the inventor performed an analysis to identify the human genes associated with the pI3K-AKT signaling pathway, the mRNA of which would be targets for circulating microRNA 411 :

Table 14

In the following Table 15, the inventor performed an analysis to identify the human genes associated with the AMPK signaling pathway, the mRNA of which would be targets for circulating microRNA 411 :

Table 15

In the following Table 16, the inventor performed an analysis to identify the human genes associated with the metabolic pathway, the mRNA of which would be targets for circulating microRNA 411 :

Table 16

GLUD2 1

ETNK1 3

DGKH 9

GK 5

ITPK1 3

IDH2 1

PFKM 5

ATP5F1 6

Proposed models

In view of the data and analysis presented herein, the inventor proposes the following models.

Without being bound to any particular theory, Fig. 21 illustrates a proposed model of the continuum of Alzheimer's disease (AD) from pre -symptomatic At-Risk AD phase (ARAD) to Mild Cognitive Impairment (MCI) and three stages of bona fide Alzheimer's disease (AD). Without being bound by any theory, it is proposed that both genes and environmental factors may contribute to the risk to developing Alzheimer's disease in old age. The At-Risk AD phase (ARAD) is typified by positive family history (FH) of AD, as well as waist circumference gain in mid-life. Loss of expression level of circulating microRNA miR-27a as determined from a substantially cell free biological sample may then be one candidate microRNA biomarker for ARAD, to be distinguished from FH-negative and normal BMI/normal waist circumference controls. The rise of expression level of circulating miR-411 as determined from a substantially cell free biological sample may then be used to distinguish MCI individuals from AD patients at the mild stage of dementia. Dementia progression from mild to moderate and severe phases of AD could be staged by the rise of expression level of the microRNA miR-34c as determined from a substantially cell free biological sample.

Without being bound to any particular theory, Fig. 22 illustrates a proposed model of shared oxidative stress signaling networks between obesity and Alzheimer's disease (AD), regulated by oxidative stress- associated microRNAs (also referred herein as "Oxy-miRs"). Without being limited by any theory, it is proposed that the same Oxy-miR may suppress both insulin-like growth factor- 1 ("IGFl") and Fat mass and obesity-associated protein ("FTO"). The latter decrease may induce Iroquois-class homeodomam protein ("IRX3") increase; along with decreased adiponectin and adiponectin receptor 1 (Adipo-Rl), this constitutes pathological adiposity. Together with decreased IGF1, it may create insulin resistance and adipogenesis signaling disorder. The suppression of SIRT1 by the same Oxy-miR could also contribute to the weakening of oxidative defenses. The consequence of these selected signaling disorders may lead to the decrease of synaptic functionality and Αβ and Phospho-Tau increase, leading to the loss of functional neurons seen in AD-associated dementia.

EXAMPLE 9

The following example describes a programmable system 100 for use in determining AD, MCI, or NEC likelihood status in a subject in accordance with a specific example of implementation of the present disclosure.

With reference to Fig. 23, the system 100 is comprised of a plurality of devices interconnected over a data network 140. As shown, the system 100 is comprised of a plurality of devices interconnected over a data network 140. The plurality of devices may includes a reverse transcription real time polymerase chain reaction (RT-qPCR) apparatus 110, a clinical module 150, computing devices 160_ab associated with respective medical expert and (optionally) the subject.

Generally, the RT-qPCR apparatus 110 is configured for processing a substantially cell free fluid sample from a test subject in order to determine an expression level of a specific circulating microRNA in the sample. In accordance with the embodiment depicted, the RT-qPCR apparatus 110 is in communication with the clinical module 150 over the data network 140. It will be understood by the person of skill, however, that in other implementations, the RT-qPCR apparatus 110 may have a communication link with the clinical module 150 via optical fibers instead of, or in addition to, over the network 140.

The computing devices 160_ab associated with respective medical expert and (optionally) the subject may establish communications with the clinical module 150 over the data network 140. While two computing devices 160^ have been depicted in Fig. 23, it is to be appreciated that the system 100 may include any number of such devices. As will be described later on in the present document, in the context of the system 100 depicted in Fig. 23, a computing device 160_a or 160 may be used to receive electronic notifications originating from the clinical module 150.

The clinical module 1 0 may be configured for receiving, optionally over the data network 140, a first signal from the RT-qPCR apparatus 110, and for processing such signal to derive useful information in connection with the AD, MCI, or NEC likelihood status of the subject. In some implementations, the clinical monitoring module 150 may also be configured to transmit data to the computing devices 160_ab associated with respective medical expert and (optionally) the subject over the data network 140. A description of the functionality of the clinical module 150 will be described later on in greater detail in the present document.

In practical implementations, the system 100 of Fig. 23 may be of a distributed nature where the RT- qPCR apparatus 110, the clinical module 150 and computing devices 160_¾b may be in different locations and be interconnected through data network 140.

In practical implementations, the data network 140 may be any suitable data network including but not limited to public network (e.g., the Internet), a private network (e.g., a LAN or WAN), a wired network (e.g., Ethernet network), a wireless network (e.g., an 802.11 network or a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), routers, hubs, switches, server computers, and/or any combinations thereof.

The communication link between the plurality of devices described previously amongst themselves and/or the data network 140 can be metallic conductors, optical fibers or wireless. The specific nature of the hardware/software used to establish a communication between the plurality of devices amongst themselves and/or the data network 140 may vary between implementations and is not critical to the present invention and will therefore not be described in further detail here.

With reference to Fig. 24, there is shown an example of configuration of the clinical module 150 of the system 100 depicted in Fig. 23.

It is to be appreciated that the system 100 may include similar or different configurations for the clinical module 150 that is shown in Fig. 24 and may implement similar or different functions from those described herein below with reference to the clinical module 150. Therefore the present description should be considered as illustrating one amongst different possible implementations for the clinical module 150.

As depicted, the clinical module 150 includes a first input 1210 in communication with the RT-qPCR apparatus 110 for receiving the first signal, an apparatus 1200 implementing some tools for determining the AD, MCI, or NEC likelihood status of the test subject, an output unit 1214 for conveying information provided by the apparatus 1200 and a data communication module 1230 for allowing the apparatus 1200 to communicate with other devices over data network 140 (shown in Fig. 23).

The RT-qPCR apparatus 110 is for amplifying nucleic acid molecules in a sample and such apparatuses are well known in the art to which this invention pertains and as such will not be described further here. Clinical module 150

Optionally, the clinical module 150 may further include a user input device 1250 for receiving data from a user of the system. The data may convey commands directed to controlling various features of the tools implemented by apparatus 1200 and, optionally, may also convey various clinical data associated with the subject, such as for example (but not limited to), positive AD family history, body mass index (BMI) values, waist circumference (WC) values, blood glucose values, leptin blood levels, other circulating microRNA values, age of test subject, MMSE score of test subject, and the like. The type of data received through such a user input device may vary between different practical implementations. The user input device 1250 may include any one or a combination of the following: keyboard, pointing device, touch sensitive surface, actuator/selection switches or speech recognition unit.

The person skilled in the art will appreciate that the methods and approaches presented in the present application may be used alone or in combination with other methods and approaches to generate information for assisting clinical staff in assessing the AD, or MCI, or NEC likelihood status of a test subject.

The output unit 1214 is in communication with the apparatus 1200 and receives signals causing the output unit 1214 to convey AD, MCI, or NEC likelihood status information associated with the test subject. The output unit 1214 may be in the form of a display screen, a printer or any other suitable device for conveying to a physician or other health care professional information conveying the AD, MCI, or NEC likelihood status information associated with the test subject. In a non-limiting implementation, the output unit 1214 includes one or more display monitors to display information in a visual format based on data and/or signals provided by the apparatus 1200. The information displayed may be derived by the apparatus 1200 or may be derived by another device (e.g. the clinical monitoring module 150 shown in Fig. 23 or another device) and communicated to the apparatus 1200 over the data network 140 via the data communication module 1230. In non-limiting examples, the display monitor may be a computer screen associated with a computer workstation, the display screen of a computer tablet or the display screen of smart-phone.

The apparatus 1200 includes a processor 1206 that may be programmed to process the signal generated by the RT-qPCR apparatus 110 and the user generated data to derive information related to the test subject AD, MCI or NEC likelihood status and/or to process information received over the data network 140 and originating from devices external to the clinical module 150. The processor 1206 may also be programmed to release a signal for causing output unit 1214 to display the information related to AD, MCI or NEC likelihood status of the test subject to assist clinicians. In accordance with a specific implementation, as will be described later below, using a data communication module 1230, the processor may be programmed to establish a communication with the computing devices 160_ab associated with respective medical expert and (optionally) the subject over the data network 140 in order to communicate data conveying the AD, MCI or NEC likelihood status associated with the test subject. It is to be appreciated that the nature of the data sent over the network may vary between implementations. For example, the data sent may include an unaltered version of the signals generated by the RT-qPCR apparatus 110 and/or information received through the user input device 1250. Alternatively, or in addition, the data sent may include data derived by processing the signals generated by the RT-qPCR apparatus 110 and/or the information received through the user input device 1250 using the processor 1206 according to various methods in order to derive information pertaining to the AD, MCI or NEC likelihood status associated with the test subject.

As shown, the apparatus 1200 includes a processor 1206 and a memory 704 connected by a communication bus 708. The memory 704 includes data 710 and program instructions 706. The processing unit 702 is adapted to process the data 710 and the program instructions 706 in order to implement some of the functional blocks described in this document and depicted in the drawings. For example, the program instructions 706 when executed by the processing unit 702 may implement one or more of the processes that will be described later on in this document with reference to any one of Fig. 27.

Optionally, the data 710 stored in memory 704 may convey information associated with the subject. Such information may include patient identification information (e.g. name, age, weight, sex), site of care information (e.g. name of site of care, address, phone, local medical expert contact information (e.g. name, phone number, e-mail address, etc.) and/or clinical care information (e.g. contact information of medical expert (e.g. name, phone number, e-mail address, etc.). It is to be appreciated that the type of information stored may vary from one implementation to the other and that the type of information presented above was intended for the purpose of illustration only.

The processing unit 702 may also be programmed to establish communications over the data network 140 with one or more of the computing devices 160_{a b} (shown in Fig. 23) to transmit and/or receive information to/from the computing devices 160_ab. For example, the processing unit 702 may communicate with the computing device 160a to transmit data conveying AD, MCI or NEC likelihood status associated with the test subject. It is to be appreciated that the nature of the data sent over the network may vary between implementations. For example, the data sent may include data derived by processing the signals obtained from the RT-qPCR apparatus 110. Alternatively, or in addition, the data sent may include an electronic notification data associated with the subject, as will be further described later in this document. The processing unit 702 may also communicate with the computing device 160_a in order to receive information conveying data and/or commands, for example information conveying a request for information associated with the particular patient.

Computing device 160a,b associated with medical expert and (optionally) user

In accordance with some specific practical implementations, the computing devices 160_a may be associated with a respective medical expert and (optionally) a user.

In certain embodiments, the computing devices 160_ab can each be directly connected to the data network 140 via any suitable hardware/software components, or can be connected with each other via a private network (e.g. a Local Area Network (LAN)), which in turn, can be connected to the data network 140 (e.g. which may be a Wide Area Network (WAN) and/or a public network such as the Internet). The communication link between the computing devices 160_ab and the data network 140 can be metallic conductors, optical fibers or wireless.

In specific practical implementations, at least some of the computing devices 160_ab may be embodied as smartphones, tablets and/or networked general purpose computers programmed for implementing at least some features described in the present document. The specific nature of the hardware/software used to establish a communication between the computing devices 160_ab and the data network 140 may vary between implementations and is not critical to the present invention and will therefore not be described in further detail here.

With reference to Fig. 25, there is shown an example of a configuration of one (1) of the computing devices 160_ab of the system 100 depicted in Fig. 23, namely computing device 160_a. It is to be appreciated that the other computing devices 160_b of the system 100 may have similar of different configurations and may implement similar or different functions from those described herein below with reference to computing device 160_a and, therefore, this description should be considered as illustrating one amongst different possible implementations.

As depicted, the computing device 160_a includes a processing unit 732 and a memory 734 connected by a communication bus 738. The memory 734 includes data 740 and program instructions 736. The processing unit 732 is adapted to process the data 740 and the program instructions 736 in order to implement some of the features described in the specification and/or depicted in the drawings. In a non- limiting example, the program instructions 736 may be configured to cause the display of GUIs of the type depicted in Fig. 26.

In accordance with a specific implementation, the processing unit 732 may be programmed to establish a communication over the data network 140 with the clinical monitoring module 150. For example, the processing unit 732 can receive electronic notification data conveying AD, MCI or NEC likelihood status associated with the test subject, as will be further described later in this document. It will be appreciated that the nature of the data received over the network may vary between implementations.

The processing unit 732 may also be programmed to transmit data over the data network 140 to the clinical module 150 to request further clinical information associated with the particular test subject.

As depicted, the processing unit 732 may also include an interface 744 for receiving a control signal and/or user input information from the user of the device 160a, such as but without being limited to a request by the user for additional information associated with at least the particular test subject.

The Processes and Functionality that may be provided by the system 100

A first embodiment of a process for use in determining AD, MCI, or NEC likelihood status in a subject that may be implemented by the system 100 will now be described with reference to the flow diagram depicted in Fig. 27. Fig 27 show steps performed from the perspective of the clinical module 150.

Generally, the process shown in Fig. 27 provides for selectively transmitting electronic notifications in connection with the results obtained from testing the expression level of one or more circulating microRNA(s) in a biological sample, over the data network 140 from the clinical module 150 to a particular device 160a or 160b associated with a particular medical expert or the subject.

As shown in Fig. 27, at step 200, data conveying an expression level of a circulating microRNA associated with a subject is received at the clinical module 150. As discussed previously, this data can be received at the clinical module 150 over the data network 140 or a communication link via optical fibers instead of, or in addition to, over the network 140. With reference to the system depicted in Figure 38, the data originates from the RT-qPCR apparatus 110 which is interconnected with the clinical module 150. As discussed above, the nature of the data received may vary between different practical implementations but would typically include an expression level of circulating microRNA miR-411, or miR-34c, or miR-27a, or miR-181b.

At step 210, the clinical module 150 processes the data received at step 200 to derive information conveying respective criticality levels for the subject being tested. The respective criticality levels for the subject being tested can be expressed in any suitable manner such as for example a score, a risk levels selected from a set of risk levels, a likelihood, as a percentile value or in any other format suitable for conveying a level of risk associated with AD status or prognosis. The specific criteria and approach for deriving criticality levels may vary between practical implementations. It will also be appreciated that the set of criteria for deriving criticality levels for the subject being tested may be customizable and may evolve over time, adjusting to evolving policies or scientific advances in geriatric medicine. The specific manner in which a level of criticality of subject being tested is derived is not critical to the invention and will therefore not be described in further detail here.

At step 220, the clinical module 150 processes the respective criticality levels derived at step 210 to determine whether a positive or negative notification should be transmitted to a particular device 160a associated with a particular medical expert or 160b associated with the subject being tested. In a specific implementation, such a determination may be made by performing a comparison between the derived criticality levels and a threshold criticality level. The threshold criticality can be established by a user/owner/operator of the system 100 (or by the organisation hospital) using, e.g., a suitable tool for allowing a user to program the threshold criticality level and/or may be set to a pre-determined value at the time the system 100 is configured.

Turning now to (optional) step 725, at this step the clinical module 150 determines whether other risk factors are present. In a non-limiting example, the risk factors may have been entered by the subject being tested himself and/or by a medical staff at the user input device 1250 (shown in Figure 24).

If at step 725 it is determined that other risk factors are present, for example (but without being limited to) particular age, high BMI or WC, then the process loops back to step 210, where the clinical module 150 processes the data to derive the criticality level for the subject being tested.

Alternatively, if at step 725 it is determined that no other risk factor was received in connection with the subject being tested, the process proceeds to step 230 of transmitting the electronic notification data, which will be described below.

While the assessment of whether other risk factors were present depicted in Fig. 27 is shown as being a step distinct from steps 200 210 and 220, it will be appreciated that in some specific alternative implementation, data conveying the presence of other risk factors may be transmitted and form part of the data received at step 200 and the deriving of the criticality level at step 210 may therefore take into account receipt of such presence of other risk factors. In such non-limiting implementations, the level of criticality associated with a subject being tested derived at step 220 may be conditioned at least in part based on the presence of the other risk factors. For example, the presence of the other risk factors could affect a derived criticality level so as to have it exceed the threshold criticality level at step 220 where it would not have done so in absence of the request for consultation.

Moving now to step 230, at this steps the clinical module 150 transmits electronic notification data over the data network 140 to a particular computing device 160_a 160_b, where the electronic notification data being sent is associated with the subject being tested. Transmitting such notification data to the computing device 160_a may allow drawing the attention of the medical expert associated with the computing device 160_a to an AD risk situation associated with the subject being tested that may require medical intervention.

In some specific practical implementations, the electronic notification data may be in the form of an e- mail message and/or an SMS message and may be transmitted to a specific one of the computing devices 160_{a b}. The specific one of the computing devices 160_{a b} to which the e-mail or SMS may be sent may be determined in a number of different manners. For example, the contact information of the particular medical expert (e.g. e-mail address and/or telephone number) may be extracted from a memory, for example memory 704, of the clinical module 150. The particular medical expert may be (i) specific to the particular tested subject for which a message is being sent, (ii) associated to a plurality of patients in some logical manner (for example based on geographic proximity), (iii) selected from a pool of available medical experts using some heuristic rule (for example using a round robin type of allocation or in dependence to the criticality level) and/or (iv) determined using any other suitable approach so that the electronic notification data may be sent to a particular medical expert.

In some practical examples of implementation, the electronic notification data is configured for causing a graphical user interface (GUI) to be displayed on a display screen of the computing device 160a associated with the particular medical expert. Example of features that may be presented on such a GUI will be further described later in this document.

In some alternate specific examples of implementation (not shown in the Figures), rather than transmitting an SMS message or an e-mail, the computing devices 160_{a b} may be executing a computer program which is configured so that electronic notification data transmitted to a specific one of the computing devices 160_{a b} may cause a pop-up window including a GUI to appear on the display screen of the specific one of the computing devices 160_{a b}.

It is to be appreciated that while the process depicted in Fig. 27 contemplates the use of a single critical threshold for triggering the transmittal of a notification, it is to be appreciated that alternative embodiments may contemplate the use of multiple critical thresholds each of which may trigger respective notifications, the nature of which may vary according to the threshold. Thus, according to such variants, different threshold criticality levels may be contemplated, where exceeding each threshold may trigger different types of electronic notifications. For example, a first threshold criticality level may trigger the transmittal of electronic notification data conveying a notification of a first type (e.g. low level emergency) and be sent to a first medical expert (e.g. an geriatrics nurse). A second threshold criticality level may trigger the transmittal of electronic notification data conveying a notification of a second type (e.g. mid-level level emergency) and be sent to the same first medical expert or to a different/second medical expert (e.g. geriatrics intern). A third threshold criticality level may trigger the transmittal of electronic notification data conveying a notification of a third type (e.g. hi -level level emergency) and be sent to the same first medical expert or to the second medical expert or to yet a different/third medical expert a second medical expert (e.g. a specialist in geriatric psychiatrist, for example, and/or a medical expert that may be located in proximity to the particular patient).

For example, the notification of the first type may include an electronic notification which causes the GUI to display information regarding the circulating miR-34a having exceeded a first threshold criticality level with respect to a particular patient, and the notification of the second type may include an electronic notification which causes the GUI to display information regarding the circulating miR-411 having exceeded a second threshold criticality level.

In one non-limiting embodiment, the electronic notification data conveying the notification of the second type is transmitted to the computing device 160a associated with the particular medical expert. In another non-limiting embodiment, the electronic notification data conveying the notification of the second type is transmitted to a computing device 160b associated with a second particular medical expert, which is distinct from the first medical expert. In yet another non-limiting embodiment, the electronic notification data conveying the notification of the second type is transmitted to a computing device (not shown) associated with a clinical staff member located in proximity to the particular patient.

While not shown in Fig. 27, following the transmittal of an electronic notification at step 230, the clinical monitoring module 150 may wait for a signal from the computing device 160_a 160_b to which the electronic notification was sent confirming that the notification was received. In some implementation, failure to receive a signal confirming that the notification was received with a certain time delay from the computing device 160_a 160_b to which the notification was sent may cause the clinical monitoring module 150 to send another electronic notification (essentially repeating step 230) to either the same computing device to which the first notification was sent or to another computing device associated with another medical expert. The time delay may have fixed duration and/or may be conditioned based on the level of criticality associated with the patient. For example, the higher the criticality level, the shorter the time delay for waiting for a signal confirming that the notification was received may be.

A non-limiting example of a specific GUI that may be caused to be displayed on the display screen of the computing device 160_a is shown Fig. 26. In a specific implementation, the information elements displayed on the GUI may form an initial set of information elements associated with the particular patient.This initial set of information displayed on the GUI may be useful in attracting a user^'s attention to certain aspects of the AD / MCI status of the particular patient so that the medical expert may get a snap shot of the situation. By displaying an initial set of information element associated with the particular patient, the medical expert may monitor and analyse the AD / MCI progression of the respective patient based on a more focused, concise and informative information displayed on the graphical window 300.

Optionally, and as depicted, the GUI 300 includes user operable control component 321 to enable the user to issue a message to the clinical monitoring module 150 confirming that the electronic notification has been received and is being looked. In the specific embodiment depicted, the operable control component 321 is provided in the form of touch sensitive areas on the display however it will be appreciated that any suitable format of user operable control may be provided in alternate implementations.

As depicted, the GUI 300 also includes a set of information sections 305 and 310. Information sections 305 and 310 are in the form of text boxes conveying information such as but without being limited to identification of the particular patient, risk factor elements associated with the particular patient, and derived criticality level. The graphical window 300 may alternatively, or additionally, include a graphical information section 315, which may visually convey AD / MCI likelihood risk information elements associated with the particular patient. In practical implementations, different types of visual identifier codes may be used including, without being limited to, a color code, changes in font sizes, "blinking" displays or any other manner that may assist a user in visually distinguishing between the different types of AD / MCI elements associated with the particular patient, for example but without being limited to as to whether a given element is transient or not.

It should be understood that the window 300 is only a specific example of a specific visual representation of the type of AD / MCI assessment that can be conveyed. It is within the scope of the invention for a visual representation to contain more or less information.

In the specific example of implementation, the graphical window 300 also includes one or more user operable control components 320 325 323 to enable the user to request additional information in connection with the particular patient and/or to initiate a communication with another device. In the specific embodiment depicted, three user operable control component 320 and/or 325 and/or 323 are provided in the form of touch sensitive areas on the display however it will be appreciated that any suitable format of user operable control may be provided in alternate implementations. It will also be understood that while the above described specific example of provides user operable control components 320 323 and 325 to enable the user to request for a particular action, the reader will readily understand that there may be one or more user operable control components depending on the particular implementation. In one non-limiting embodiment, the user operable control components 320 and/or 325 and/or 323 can cause the display of a list of actions from which the user may select to request for the particular action (not shown). For example, in the GUI shown in Fig. 26. actuation of the control 325 may cause a menu to appear providing different communications options allowing the medical expert to choose amongst communications options of the type mentioned above. Optionally, the types of the selectable options made available through the menu may be dynamically adaptable so as to present the medical expert with options customized to particular circumstances associated with the patient. For example, in a case where the electronic notification data was sent to computing device 160_a, the selectable options may include a telephone call and a video call in connection with a device located at the patient's bedside but may exclude an audio alarm trigger and/or a visual alarm trigger to reduce the likelihood the medical expert may trigger alarms unnecessarily. As another example, in a case where the electronic notification data was sent in part as a result of a criticality level exceeding a threshold, the selectable options may include an audio alarm trigger and/or a visual alarm trigger in addition to other options.

Practical Implementation

A practical illustrative implementation will be further described with respect to information derived from measuring the expression level of circulating miR-411 in a substantially cell free sample from a subject.

At step 200, the system 100 receives from an RT-qPCR apparatus 110, a first signal indicative of the amplification of circulating microRNA miR-411 from a substantially cell free sample. Note that the substantially cell free sample may be a plasma sample which has been processed to ensure that there are substantially no cells in the sample. At step 210, the system 100 processes the first signal to derive a criticality level based on a comparison of an expression level of the circulating microRNA in the sample with a threshold reference level being associated with a cohort including at least 10 reference subject.

At step 725, the system receives an optional second signal derived from user generated data relating to the test subject having a family history of AD, a given age, a given plasma leptin and/or glucose level, etc. When the second signal is received, this signal is also processed by the system 100 at step 210 to derive the criticality level. At step 220, the system processes the criticality level signal to derive a likelihood risk value by comparing the criticality level in the sample with a threshold criticality level stored in memory. At step 230, the system 100 selectively causes an output signal to be released via the output, the output signal being indicative of an AD / MCI / NEC likelihood status and being derived at least in part by processing the outcome of said comparison and said risk likelihood value.

Specific Physical Implementation

Those skilled in the art should appreciate that in some embodiments of the invention, all or part of the functionality for previously described herein with reference to the clinical module 150 and/or the devices 160_{a b} may be implemented as pre-programmed hardware or firmware elements (e.g., application specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), etc.), or other related components.

In other embodiments of the invention, all or part of the functionality previously described herein may be implemented as computer program products including instructions that, when executed, cause a programmable sy stem including at least one programmable processor to perform operations. In practical implementations, the program product could be stored on a medium which is fixed (non-transitory), tangible and readable directly by the programmable system, (e.g., removable diskette, CD-ROM, ROM, PROM, EPROM, flash memory or fixed disk), or the instructions could be stored remotely but be transmittable to the programmable system via a modem or other interface device (e.g., a communications adapter) connected to a network over a transmission medium. The transmission medium may be either a wired medium (e.g., optical or analog communications lines) or a medium implemented using wireless techniques (e.g., microwave, infrared or other transmission schemes).

The phrases "connected to" and "in communication with" refer to any form of interaction between two or more entities, including mechanical, electrical, magnetic, and electromagnetic interaction. Two components may be connected to each other even though they are not in direct contact with each other and even though there may be intermediary devices between the two components.

Other examples of implementations will become apparent to the reader in view of the teachings of the present description and as such, will not be further described here.

Note that titles or subtitles may be used throughout the present disclosure for convenience of a reader, but in no way these should limit the scope of the invention. Moreover, certain theories may be proposed and disclosed herein; however, in no way they, whether they are right or wrong, should limit the scope of the invention so long as the invention is practiced according to the present disclosure without regard for any particular theory or scheme of action.

All references cited throughout the specification are hereby incorporated by reference in their entirety for all purposes.

It will be understood by those of skill in the art that throughout the present specification, the term "a" used before a term encompasses embodiments containing one or more to what the term refers. It will also be understood by those of skill in the art that throughout the present specification, the term "comprising", which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, un-recited elements or method steps. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the case of conflict, the present document, including definitions will control.

As used in the present disclosure, the terms "around", "about" or "approximately" shall generally mean within the error margin generally accepted in the art. Hence, numerical quantities given herein generally include such error margin such that the terms "around", "about" or "approximately" can be inferred if not expressly stated.

Although the present invention has been described in considerable detail with reference to certain embodiments thereof, variations and refinements are possible and will become apparent to persons skilled in the art in light of the present description.

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Claims

CLAIMS:

1. A system, comprising: a reverse transcription real time polymerase chain reaction (RT-qPCR) apparatus for processing a substantially cell free biological sample from a subject to obtain an amplification of circulating microRNA miR-411, wherein said RT-qPCR apparatus is configured for generating a signal indicative of the amplification of the circulating microRNA; and an apparatus having an input in communication with said RT-qPCR apparatus for receiving said signal; a processing unit; a memory; and an output; said processing unit being programmed for: processing the signal to derive an expression level of the circulating microRNA in the sample; comparing the expression level of the microRNA in the sample to a reference level of the microRNA stored in the memory; and causing an output signal to be released via the output, the output signal being indicative of an Alzheimer's Disease (AD) or a mild cognitive impairment (MCI) or normal elderly control (NEC) likelihood status of the subject at least being based on an outcome of said comparison.

2. The system of claim 1, wherein said biological sample is serum or plasma.

3. The system of claim 1 or 2, wherein said reference level is derived from AD reference subjects.

4. The system of any one of claims 1 to 3, wherein said reference level is derived from MCI reference subjects.

5. The system of any one of claims 1 to 3, wherein said reference level is derived from NEC reference subjects.

6. The system of any one of claims 1 to 5, wherein said reference level is derived from at least 10 respective reference subjects.

7. The system of any one of claims 1 to 6, wherein said output signal is released and transmitted over a data network in connection with the system to a computing device.

8 The system of claim 7, wherein the computing device is associated with a medical expert.

9. The system of claim 7, wherein the computing device is associated with the subject.

10. A computer program product comprising one or more tangible non-transitory computer readable storage media storing computer executable instructions for receiving information associated with a subject over a data network, the computer executable instructions, when executed, cause a programmable system including at least one programmable processor and a memory to perform operations, the operations comprising: a) receiving data over the data network from one or more computing devices interconnected with the programmable system over the data network, the received data conveying information on an expression level of circulating microRNA miR-411 in a substantially cell free sample of the subject; b) at the programmable system, processing the data conveying the expression level of the circulating microRNA miR-411 in the substantially cell free sample of the subject to derive a criticality level for the subject, the criticality level being derived at least in part by comparing the expression level of the circulating microRNA miR-411 to a reference level of the microRNA stored in said memory; c) selectively transmitting electronic notification data over the data network in connection with the patient following the criticality level associated with the subject exceeding a threshold criticality level, the electronic notification data being transmitted to a computing device.

11. The computer program product of claim 10, wherein said biological sample is serum or plasma.

12. The computer program product of claim 10 or 11, wherein said reference level is derived from AD reference subjects.

13. The computer program product of any one of claims 10 to 12, wherein said reference level is derived from MCI reference subjects.

14. The computer program product of any one of claims 10 to 12, wherein said reference level is derived from NEC reference subjects.

15. The computer program product of any one of claims 10 to 12, wherein said reference level is derived from at least 10 respective reference subjects.

16. A method for determining an Alzheimer's disease (AD) likelihood status in a subject, the method comprising obtaining a substantially cell free biological sample from the subject, measuring an expression level of circulating microRNA miR-181c in the sample, and comparing the expression level of the circulating microRNA with a reference level of the microRNA to establish the likelihood of AD in the subject.

17. The method of claim 16, wherein said biological sample is serum or plasma.

18. The method of claim 16 or 17, wherein said reference level is derived from AD reference subjects.

19. The method of any one of claims 16 to 18, wherein said reference level is derived from mild cognitive impairment (MCI) reference subjects.

20. The method of any one of claims 16 to 18, wherein said reference level is derived from normal elderly control (NEC) reference subjects.

21. The method of any one of claims 16 to 20, wherein said reference level is derived from at least 10 respective reference subjects.

22. The method of any one of claims 16 to 21, wherein said measuring is carried out by RT-qPCR.

23. A method for determining a likelihood of Alzheimer's disease (AD) or a mild cognitive impairment (MCI) likelihood status in a subject, the method comprising obtaining a substantially cell free biological sample from the subject, measuring an expression level of circulating microRNA miR-411 in the sample, and comparing the expression level of the circulating microRNA with a reference level of the microRNA to establish the AD or MCI likelihood status in the subject.

24. The method of claim 23, wherein said biological sample is serum or plasma.

25. The method of claim 23 or 24, wherein said reference level is derived from AD reference subjects.

26. The method of any one of claims 23 to 25, wherein said reference level is derived from MCI reference subjects.

27. The method of any one of claims 23 to 25, wherein said reference level is derived from normal elderly control (NEC) reference subjects.

28. The method of any one of claims 23 to 26, wherein said reference level is derived from at least 10 respective reference subjects.

29. The method of any one of claims 23 to 28, wherein said measuring is carried out by RT-qPCR.

30. A method for assisting in prognosis of late-life Alzheimer's disease (AD) likelihood in a subject, comprising obtaining a substantially cell free biological sample of the subject, the subject being aged in the range of 40 to 69 years of age, measuring an expression level of circulating microRNA miR-34c or miR-34a in the sample, and comparing said expression level with a reference level of the microRNA to establish the prognosis likelihood of late-life AD of the subject.

31. The method of claim 30, wherein said biological sample is serum or plasma.

32. The method of claim 30 or 31, wherein said reference level is derived from reference subjects having a positive AD family history.

33. The method of claim 30 or 31, wherein said reference level is derived from reference subjects having a negative AD family history.

34. The method of any one of claims 30 to 33, wherein said reference level is derived from reference subjects having a high body mass index (BMI) and/or high waist circumference (WC).

35. A method for assisting in prognosis of late-life Alzheimer's disease (AD) likelihood in a subject, comprising obtaining a substantially cell free biological sample of the subject, the subject being aged in the range of 40 to 69 years of age, measuring an expression level of circulating microRNA miR-27a in the sample, and comparing said expression level with a reference level of the microRNA to establish the prognosis likelihood of late-life AD of the subject

36. The method of claim 35, wherein said biological sample is serum or plasma.

37. The method of claim 35 or 36, wherein said reference level is derived from reference subjects having a positive AD family history.

38. The method of claim 35 or 36, wherein said reference level is derived from reference subjects having a negative AD family history.

39. A system, comprising: c) a reverse transcription real time polymerase chain reaction (RT-qPCR) apparatus for processing a substantially cell free biological sample from a subject to obtain an amplification of circulating microRNA miR-27a, wherein said RT-qPCR apparatus is configured for generating a signal indicative of the amplification of the circulating microRNA; and d) an apparatus having an input in communication with said RT-qPCR apparatus for receiving said signal; a processing unit; a memory; and an output; said processing unit being programmed for: i. processing the signal to derive an expression level of the circulating microRNA in the sample; ii. comparing the expression level of the microRNA in the sample to a reference level of the microRNA stored in the memory; and iii. causing an output signal to be released via the output, the output signal being indicative of a prognosis of late life Alzheimer's Disease (AD) likelihood status of the subject at least being based on an outcome of said comparison.

40. The system of claim 39, wherein said biological sample is serum or plasma.

41. The system of claim 39 or 40, wherein said reference level is derived from reference subjects having a positive AD family history.

42. The system of claim 39 or 40, wherein said reference level is derived from reference subjects having a negative AD family history.

43. The system of any one of claims 39 to 42, wherein said reference level is derived from at least 10 respective reference subjects.

44. The system of any one of claims 39 to 43, wherein said output signal is released and transmitted over a data network in connection with the system to a computing device.

45. The system of claim 44, wherein the computing device is associated with a medical expert.

46. The system of claim 44, wherein the computing device is associated with the subject.

47. A computer program product comprising one or more tangible non-transitory computer readable storage media storing computer executable instructions for receiving information associated with a subject over a data network, the computer executable instructions, when executed, cause a programmable system including at least one programmable processor and a memory to perform operations, the operations comprising: a) receiving data over the data network from one or more computing devices interconnected with the programmable system over the data network, the received data conveying information on an expression level of circulating microRNA miR-27a in a substantially cell free sample of the subject; b) at the programmable system, processing the data conveying the expression level of the circulating microRNA miR-27a in the substantially cell free sample of the subject to derive a criticality level for the subject, the criticality level being derived at least in part by comparing the expression level of the circulating microRNA miR-27a to a reference level of the microRNA stored in said memory; c) selectively transmitting electronic notification data over the data network in connection with the patient following the criticality level associated with the subject exceeding a threshold criticality level, the electronic notification data being transmitted to a computing device.

48. The computer program product of claim 47, wherein said biological sample is serum or plasma.

49. The computer program product of claim 47 or 48, wherein said reference level is derived from reference subjects having a positive AD family history.

50. The computer program product of claim 47 or 48, wherein said reference level is derived from reference subjects having a negative AD family history.

51. The computer program product of any one of claims 47 to 50, wherein said reference level is derived from at least 10 respective reference subjects.

52. A system, comprising: e) a reverse transcription real time polymerase chain reaction (RT-qPCR) apparatus for processing a substantially cell free biological sample from a subject to obtain an amplification of circulating microRNA miR-34c or 34a, wherein said RT-qPCR apparatus is configured for generating a signal indicative of the amplification of the circulating microRNA; and f) an apparatus having an input in communication with said RT-qPCR apparatus for receiving said signal; a processing unit; a memory; and an output; said processing unit being programmed for: i. processing the signal to derive an expression level of the circulating microRNA in the sample; ii. comparing the expression level of the microRNA in the sample to a reference level of the microRNA stored in the memory; and iii. causing an output signal to be released via the output, the output signal being indicative of a prognosis of late life Alzheimer's Disease (AD) likelihood status of the subject at least being based on an outcome of said comparison.

53. The system of claim 52, wherein said biological sample is serum or plasma.

54. The system of claim 52 or 53, wherein said reference level is derived from reference subjects having a positive AD family history.

55. The system of claim 52 or 53, wherein said reference level is derived from reference subjects having a negative AD family history.

56. The system of any one of claims 52 to 55, wherein said reference level is derived from high body mass index (BMI) and/or high waist circumference (WC) reference subjects.

57. The system of any one of claims 52 to 56, wherein said reference level is derived from at least 10 respective reference subjects.

58. The system of any one of claims 52 to 57, wherein said output signal is released and transmitted over a data network in connection with the system to a computing device.

59. The system of any one of claims 52 to 58, wherein the computing device is associated with a medical expert.

60. The system of any one of claims 52 to 58, wherein the computing device is associated with the subject.

61. A computer program product comprising one or more tangible non-transitory computer readable storage media storing computer executable instructions for receiving information associated with a subject over a data network, the computer executable instructions, when executed, cause a programmable system including at least one programmable processor and a memory to perform operations, the operations comprising: a) receiving data over the data network from one or more computing devices interconnected with the programmable system over the data network, the received data conveying information on an expression level of circulating microRNA miR-34c in a substantially cell free sample of the subject; b) at the programmable system, processing the data conveying the expression level of the circulating microRNA miR-27a in the substantially cell free sample of the subject to derive a criticality level for the subject, the criticality level being derived at least in part by comparing the expression level of the circulating microRNA miR-27a to a reference level stored in said memory; c) selectively transmitting electronic notification data over the data network in connection with the patient following the criticality level associated with the subject exceeding a threshold criticality level, the electronic notification data being transmitted to a computing device.

62. The system of claim 61, wherein said biological sample is serum or plasma.

63. The system of claim 61 or 62, wherein said reference level is derived from reference subjects having a positive AD family history.

63. The system of claim 61 or 62, wherein said reference level is derived from reference subjects having a negative AD family history.

64. The system of any one of claims 61 to 63, wherein said reference level is derived from high body mass index (BMI) and/or high waist circumference (WC) reference subjects.

65. The system of any one of claims 61 to 64, wherein said reference level is derived from at least 10 respective reference subjects.

66. The system of any one of claims 61 to 65, wherein said output signal is released and transmitted over a data network in connection with the system to a computing device.

67. The system of any one of claims 61 to 66, wherein the computing device is associated with a medical expert.

68. The system of any one of claims 61 to 66, wherein the computing device is associated with the subject.