You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 thrombosis_prediction
【Schema】
# Table: Laboratory
[
(ID:INTEGER, Primary Key, Examples: [27654, 30609, 43003]),
(Date:DATE, Primary Key, Examples: [1991-09-11]),
(GOT:INTEGER, Examples: [34, 29, 26]),
(GPT:INTEGER, Examples: [36, 31, 22]),
(LDH:INTEGER, Examples: [567, 579, 684]),
(ALP:INTEGER, Examples: [166, 154, 138]),
(TP:REAL, Examples: [4.5, 5.1, 5.5]),
(ALB:REAL, Examples: [3.3, 3.4, 3.6]),
(UA:REAL, Examples: [3.8, 4.2, 4.9]),
(UN:INTEGER, Examples: [29, 36, 34]),
(CRE:REAL, Examples: [0.8, 0.9, 0.7]),
(T-BIL:REAL, Examples: [0.3, 0.2, 0.4]),
(T-CHO:INTEGER, Examples: [165, 134, 140]),
(TG:INTEGER, Examples: [185, 205, 254]),
(CPK:INTEGER, Examples: [9, 10, 11]),
(GLU:INTEGER, Examples: [88, 134, 161]),
(WBC:REAL, Examples: [5.0, 10.4, 10.5]),
(RBC:REAL, Examples: [2.6, 2.9, 3.4]),
(HGB:REAL, Examples: [6.4, 6.7, 7.9]),
(HCT:REAL, Examples: [20.3, 21.6, 24.7]),
(PLT:INTEGER, Examples: [227, 242, 233]),
(PT:REAL, Examples: [11.3, 12.0, 11.8]),
(APTT:INTEGER, Examples: [108, 84, 76]),
(FG:REAL, Examples: [27.0, 38.6, 27.6]),
(PIC:INTEGER, Examples: [320, 513, 284]),
(TAT:INTEGER, Examples: [77, 108, 115]),
(TAT2:INTEGER, Examples: [113, 88, 104]),
(U-PRO:TEXT, Examples: [-, 0, TR]),
(IGG:INTEGER, Examples: [339, 771, 430]),
(IGA:INTEGER, Examples: [145, 188, 118]),
(IGM:INTEGER, Examples: [46, 132, 56]),
(CRP:TEXT, Examples: [0.6, 2.7, 1.2]),
(RA:TEXT, Examples: [-, +, +-]),
(RF:TEXT, Examples: [<20.5, <20.8, <40]),
(C3:INTEGER, Examples: [30, 82, 103]),
(C4:INTEGER, Examples: [14, 26, 35]),
(RNP:TEXT, Examples: [0, 1, 256]),
(SM:TEXT, Examples: [0, 1, negative]),
(SC170:TEXT, Examples: [16, 1, 0]),
(SSA:TEXT, Examples: [0, negative, 16]),
(SSB:TEXT, Examples: [0, negative, 1]),
(CENTROMEA:TEXT, Examples: [0, negative]),
(DNA:TEXT, Examples: [41.9, 18, 4.3]),
(DNA-II:INTEGER)
]
# Table: Patient
[
(ID:INTEGER, Primary Key, Examples: [2110, 11408, 12052]),
(SEX:TEXT, Examples: [F, M]),
(Birthday:DATE, Examples: [1934-02-13]),
(Description:DATE, Examples: [1994-02-14]),
(First Date:DATE, Examples: [1993-02-10]),
(Admission:TEXT, Examples: [+, -, +(]),
(Diagnosis:TEXT, Examples: [RA susp., PSS, SLE])
]
# Table: Examination
[
(ID:INTEGER, Examples: [14872, 48473, 102490]),
(Examination Date:DATE, Examples: [1997-05-27]),
(aCL IgG:REAL, Examples: [1.3, 4.3, 2.3]),
(aCL IgM:REAL, Examples: [1.6, 4.6, 2.5]),
(ANA:INTEGER, Examples: [256, 0, 16]),
(ANA Pattern:TEXT, Examples: [P, P,S, S]),
(aCL IgA:INTEGER, Examples: [0, 3, 4]),
(Diagnosis:TEXT, Examples: [MCTD, AMI, SLE, PSS]),
(KCT:TEXT, Examples: [-, +]),
(RVVT:TEXT, Examples: [-, +]),
(LAC:TEXT, Examples: [-, +]),
(Symptoms:TEXT, Examples: [AMI, CNS lupus, Apo]),
(Thrombosis:INTEGER, Examples: [1, 0, 2])
]
【Foreign keys】
Laboratory.ID=Patient.ID
Examination.ID=Patient.ID

    【Question】
    What is the average index of the lactate dehydrogenase (LDH) for all patients with lactate dehydrogenase (LDH) within the normal range. average index of the lactate dehydrogenase (LDH) refers to AVG(LDH); (LDH) within the normal range refers to LDH < 500

    【Evidence】
    average index of the lactate dehydrogenase (LDH) refers to AVG(LDH); (LDH) within the normal range refers to LDH < 500

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT AVG(LDH) FROM Laboratory WHERE LDH < 500