Runstats utility in DB2 generates statistics about the distribution of data in tables and indexes. These statistics, also known as histograms or distribution statistics, help the DB2 optimizer to make informed decisions when creating query execution plans. By analyzing these statistics, the optimizer can choose the most efficient access paths and join methods, resulting in improved query performance. Understanding and utilizing these statistics effectively is crucial for optimizing query performance in DB2. 

Creating appropriate indexes for a complex query involving multiple joins on large tables can significantly improve performance. Indexes reduce disk I/O and speed up data retrieval by providing direct access paths to the rows in the tables. This eliminates the need for full table scans and allows the database engine to quickly locate the relevant rows. Additionally, indexes enhance data organization and storage by physically sorting the rows based on the indexed columns, making it easier to retrieve data efficiently. This optimization also reduces contention and improves concurrency by enabling multiple transactions to access the same data simultaneously without blocking each other. Furthermore, indexes contribute to data integrity and consistency by enforcing constraints on the indexed columns, ensuring that only valid and consistent data is stored. 

User-Defined Functions (UDFs) in DB2 are specifically designed to handle complex calculations and data manipulation. They encapsulate reusable logic that can be invoked within SQL statements, making them the most suitable choice for implementing business logic requiring complex calculations. 

LOB data types in DB2 are used to store large binary data, such as images, audio files, and video files. These data types are designed to efficiently handle large amounts of data while providing flexibility and performance in accessing and manipulating them. 

DB2 offers advanced capabilities for data compression, including dictionary-based compression, which identifies repetitive patterns in the data and stores them more efficiently. This reduces storage requirements and can improve query performance by reducing the amount of data that needs to be read from disk. 

Multi-dimensional clustering (MDC) in DB2 improves performance by organizing data in clusters. MDC allows data to be physically organized in multiple dimensions, enabling efficient retrieval of data based on various criteria, such as range queries, which enhances performance significantly. 

Data Partitioning can help distribute data across multiple physical storage devices or servers, allowing parallel processing of queries and accommodating increased user requests. It enhances scalability by reducing contention for resources and improving overall system performance. 

Indexes with INCLUDE columns in DB2 can optimize query performance. By including additional non-key columns in the index, DB2 can cover more queries, reducing the need to access the base table and improving query performance. 

Constraints in DB2 are primarily responsible for enforcing data integrity by specifying rules or conditions that data must adhere to. This ensures that only valid data is entered into the database, contributing to its reliability and accuracy. 

Optimizing performance when using the LOAD utility in DB2 involves various considerations. These include properly configuring buffer sizes to efficiently manage data transfer, minimizing network latency to expedite communication between the database and external storage, utilizing solid-state drives (SSDs) for faster data access, and employing multi-threading to parallelize the loading process and utilize available system resources effectively.