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】
    Please provide the diagnosis of patients with ALT glutamic pylvic transaminase beyond the normal range by ascending order of their date of birth. ALT glutamic pylvic transaminase beyond the normal range refers to GPT > 60; The larger the birthday value, the younger the person is, and vice versa;

    【Evidence】
    ALT glutamic pylvic transaminase beyond the normal range refers to GPT > 60; The larger the birthday value, the younger the person is, and vice versa; 

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

SQL: SELECT DISTINCT T1.Diagnosis FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.GPT > 60 ORDER BY T1.Birthday ASC